Thinking about the Future 3 - Principles PDF

Abiliti: Future Systems

Thinking about the Future WIP Draft version 3.0

THINKING ABOUT THE FUTURE. The way that we think about the future must mirror how the future actually unfolds. As we have all learned from recent experience, the future is not a simple extrapolation of linear,

single-domain trends. We now have to consider ways in which the possibility of random, chaotic and radically disruptive events may be

factored into enterprise strategy development, threat assessment and risk management frameworks and incorporated into enterprise decision-

making structures and processes.


• Abiliti: Origin Automation is part of a global consortium of Digital Technologies Service Providers and Future Management Strategy Consulting firms for Digital Marketing and Multi-channel Retail / Cloud Services / Mobile Devices / Big Data / Social Media

• Graham Harris Founder and MD @ Abiliti: Future Systems

– Email: (Office) – Telephone: (Mobile)

• Nigel Tebbutt 奈杰尔泰巴德

– Future Business Models & Emerging Technologies @ Abiliti: Future Systems – Telephone: +44 (0) 7832 182595 (Mobile) – +44 (0) 121 445 5689 (Office) – Email: [email protected] (Private)

• Ifor Ffowcs-Williams CEO, Cluster Navigators Ltd & Author, “Cluster Development” – Address : Nelson 7010, New Zealand (Office)

– Email : [email protected]

Abiliti: Origin Automation Strategic Enterprise Management (SEM) Framework ©

Cluster Theory - Expert Commentary: -

mailto:[email protected]





Slow is smooth, smooth is fast.....

.....advances in “Big Data” have lead to a revolution in

futures studies, forecasting and predictive modelling – but

it takes both human ingenuity, and time, for long-range

Models of the Future to develop and mature.....

At the very Periphery of Corporate Vision and Awareness…..

• The Cosmology Revolution – new and exciting advances in Astrophysics and Cosmology (String Theory and Wave Mechanics) is leading Physicists towards new questions and answers concerning the make-up of stellar clusters and galaxies, stellar populations in different types of galaxy, and the relationships between high-stellar populations and local clusters. What are the implications for galactic star-formation histories and relative stellar formation times – overall, resolved and unresolved – and their consequent impact on the evolution of life itself ?.

• The Quantum Revolution – The quantum revolution could turn many ideas of science fiction into science fact - from meta-materials with mind-boggling properties such as invisibility, limitless quantum energy via room temperature superconductors an onwards and upwards to Arthur C Clarke's space elevator. Some scientists even forecast that in the latter half of the century everybody will have a personal fabricator that re-arranges molecules to produce everything from almost anything. How ultimately will we use this gift? Will we have the wisdom to match our mastery of matter like Solomon? Or will we abuse our technology strength and finally bring down the temple around our ears like Samson?

• The Nano-Revolution – To meet the challenges in an ever more resource-limited world, innovation and technology must play an increasing role. Nanotechnology, the engineering of matter at the atomic scale to create materials with unique properties and capabilities, will play a significant part in ensuring that risks to critical water resources for future cities are addressed. Nanotechnology “has the potential to be a key element in providing effective, environmentally sustainable solutions for supplying potable water for human use and clean water for agricultural and industrial uses.”


• The Energy Revolution • Oil Shale • Kerogen • Tar Sands • Methane Hydrate • The

Hydrogen Economy • Nuclear Fusion • Every year we consume the quantity of Fossil

Fuel energy which took nature 3 million tears to create. Unsustainable fossil fuel energy

dependency based on Carbon will eventually be replaced by the Hydrogen Economy

and Nuclear Fusion. The conquest of hydrogen technology, the science required to

support a Hydrogen Economy (to free up humanity from energy dependency) and

Nuclear Fusion (to free up explorers from gravity dependency) is the final frontier which,

when crossed, will enable inter-stellar voyages of exploitation across our Galaxy.

• Nuclear Fusion requires the creation and sustained maintenance of the enormous

pressures and temperatures to be found at the Sun’s core This is a most challenging

technology that scientists here on Earth are only now just beginning to explore and

evaluate its extraordinary opportunities. To initiate Nuclear Fusion requires creating the

same conditions right here on Earth that are found the very centre of the Sun. This

means replicating the environment needed to support quantum nuclear processes which

take place at huger temperatures and immense pressures in the Solar core – conditions

extreme enough to overcome the immense nuclear forces which resist the collision and

fusion of two deuterium atoms (heavy hydrogen – one proton and one neutron) to form a

single Helium atom – accompanied by the release of a vast amount of Nuclear energy.


• Renewable Resources • Solar Power • Tidal Power • Hydro-electricity • Wind Power • The Hydrogen Economy • Nuclear Fusion • Any natural resource is a renewable resource if it is replenished by natural processes at a rate compatible with or faster than its rate of consumption by human activity or other natural uses or attrition. Some renewable resources - solar radiation, tides, hydroelectricity, wind – can also classified as perpetual resources, in that they can never be consumed at a rate which is in excess of their long-term availability due to natural processes of perpetual renewal. The term renewable resource also carries the implication of prolonged or perpetual sustainability for the absorption, processing or re-cycling of waste products via natural ecological and environmental processes.

• For the purposes of Nuclear Fission, Thorium may in future replaced enriched Uranium-235. Thorium is much more abundant, far easier to mine, extract and process and far less dangerous than Uranium. Thorium is used extensively in Biomedical procedures, and its radioactive decay products are much more benign.

• Sustainability is a characteristic of a process or mechanism that can be maintained indefinitely at a certain constant level or state – without showing any long-term degradation, decline or collapse.. This concept, in its environmental usage, refers to the potential longevity of vital human ecological support systems - such as the biosphere, ecology, the environment the and man-made systems of industry, agronomy, agriculture, forestry, fisheries - and the planet's climate and natural processes and cycles upon which they all depend.


• Trans-humanism – advocates the ethical use of technology to extend current human form and function - supporting the use of future science and technology to enhance the human genome capabilities and capacities in order to overcome undesirable and unnecessary aspects of the present human condition.

• The Intelligence Revolution – Artificial Intelligence will revolutionise homes, workplaces and lifestyles. Augmented Reality will create new virtual worlds – such as the interior of Volcanoes or Nuclear Reactors, the bottom of the Ocean or the surface of the Moon, Venus or Mars - so realistic they will rival the physical world. Robots with human-level intelligence may finally become a reality, and at the ultimate stage of mastery, we'll even be able to merge human capacities with machine intelligence and attributes – via the man-machine interface.

• The Biotech Revolution – Genome mapping and Genetic Engineering is now bringing doctors and scientists towards first discovery, and then understanding, control, and finally mastery of human health and wellbeing. Digital Healthcare and Genetic Medicine will allow doctors and scientists to positively manage successful patient outcomes – even over diseases previously considered fatal. Genetics and biotechnology promise a future of unprecedented health, wellbeing and longevity. DNA screening could diagnose and gene therapy prevent or cure many diseases. Thanks to laboratory-grown tissues and organs, the human body could be repaired as easily as a car, with spare parts readily available to order. Ultimately, the ageing process itself could ultimately be slowed or even halted.


• Global Massive Change is an evaluation of global capacities and limitations. It includes both utopian and dystopian views of the emerging world future state, in which climate, the environment, ecology and even geology are dominated by the indirect impact of human activity and the direct impact of human manipulation: –

1. Human Impact is now the major factor in climate change, environmental and

ecological degradation.

2. Environmental Degradation - man now moves more rock and earth than do all of the natural geological processes

3. Ecological Degradation – biological extinction rate - is currently greater than that of the Permian-Triassic boundary (PTB) extinction event

4. Food, Energy, Water (FEW) Crisis – increasing scarcity of Natural Resources

• Society’s growth-associated impacts on its own ecological and environmental support systems, for example intensive agriculture causing exhaustion of natural resources by the Mayan and Khmer cultures, de-forestation and over-grazing causing catastrophic ecological damage and resulting in climatic change – for example, the Easter Island culture, the de-population of upland moors and highlands in Britain from the Iron Age onwards – including the Iron Age retreat from northern and southern English uplands, the Scottish Highland Clearances and replacement of subsistence crofting by deer and grouse for hunting and sheep for wool on major Scottish Highland Estates and the current sub-Saharan de-forestation and subsequent desertification by semi-nomadic pastoralists

The Management of Uncertainty A Brief History of Chaos…..

Mechanical Processes –

Thermodynamics (Complexity and Chaos Theory) – governs the behaviour of Systems Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures

Wave Mechanics (String Theory) – integrates the behaviour of every size and type of object

Executive Summary - The Management of Uncertainty

• It has long been recognized that one of the most important competitive factors for any

organization to master is the management of uncertainty. Uncertainty is the major

intangible factor contributing towards the risk of failure in every process, at every level,

in every type of business. The way that we think about the future must mirror how the

future actually unfolds. As we have learned from recent experience, the future is not a

straightforward extrapolation of simple, single-domain trends. We now have to consider

ways in which the possibility of random, chaotic and radically disruptive events may be

factored into enterprise threat assessment and risk management frameworks and

incorporated into decision-making structures and processes.

• Managers and organisations often aim to “stay focused” and maintain a narrow

perspective in dealing with key business issues, challenges and targets. A

concentration of focus may risk overlooking Weak Signals indicating potential issues

and events, agents and catalysts of change. Such Weak Signals – along with their

resultant Wild Card and Black Swan Events - represent early warning of radically

disruptive future global transformations – which are even now taking shape at the very

periphery of corporate awareness, perception and vision – or just beyond.


• There are many kinds of Stochastic or Random processes that impact on every area

of Nature and Human Activity. Randomness can be found in Science and Technology

and in Humanities and the Arts. Random events are taking place almost everywhere

we look – for example from Complex Systems and Chaos Theory to Cosmology and

the distribution and flow of energy and matter in the Universe, from Brownian motion

and quantum theory to fractal branching and linear transformations. There are further

examples – atmospheric turbulence in Weather Systems and Climatology, and system

dependence influencing complex orbital and solar cycles. Other examples include

sequences of Random Events, Weak Signals, Wild Cards and Black Swan Events

occurring in every aspect of Nature and Human Activity – from the Environment and

Ecology - to Politics, Economics and Human Behaviour and in the outcomes of current

and historic wars, campaigns, battles and skirmishes - and much, much more.

• These Stochastic or Random processes are agents of change that may precipitate

global impact-level events which either threaten the very survival of the organisation -

or present novel and unexpected opportunities for expansion and growth. The ability to

include Weak Signals and peripheral vision into the strategy and planning process may

therefore be critical in contributing towards the continued growth, success, wellbeing

and survival of both individuals and organisations at the micro-level – as well as cities,

states and federations at the macro-level - as witnessed in the rise and fall of empires.


Random Processes

• Random Processes may influence any natural and human phenomena, such as: -

– the history of an object

– the outcome of an event

– the execution of a process

• Randomness may be somewhat difficult to demonstrate, as true Randomness in chaotic

system behaviour is not always readily or easily distinguishable from any of the “noise”

that we may find in Complex Systems – such as foreground and background wave

harmonics, resonance and interference. Complex Systems may be influenced by both

internal and external factors which remain hidden – either unrecognised or unknown.

These hidden and unknown factors may exist far beyond our ability to detect them – but

nevertheless, still exert influence. The existence of weak internal or external forces acting

on systems may not be visible to the observer – these subliminal temporal forces can

influence Complex System behaviour in such a way that the presence of imperceptibly tiny

inputs, acting on a system, amplified in effect over many system cycles - are ultimately

able to create massive observable changes to outcomes in complex system behaviour.


• Uncertainty is the outcome of the disruptive effect that chaos and randomness

introduces into our daily lives. Research into stochastic (random) processes looks

towards how we might anticipate, prepare for and manage the chaos and uncertainty

which acts on complex systems – including natural systems such as Cosmology and

Climate, as well as human systems such as Politics and the Economy – so that we may

anticipate future change and prepare for it…..

1. Classical Mechanics - Any apparent randomness is as a result of Unknown Forces

2. Thermodynamics - Randomness, chaos and uncertainty is directly a result of Entropy

3. Biology - Any apparent randomness is as a result of Unknown Forces

4. Chemistry - Any apparent randomness is as a result of Unknown Forces

5. Atomic Theory - All events are utterly and unerringly predictable (Dirac Equation)

6. Quantum Mechanics - Every event is both symmetrical and random (Hawking Paradox)

7. Geology - Any randomness or asymmetry is a result of Unknown Forces

8. Astronomy - Any randomness or asymmetry is a result of Unknown Forces

9. Cosmology - Any randomness or asymmetry is as a result of Dark Matter, Energy, Flow

10. Relativity Theory - Randomness or asymmetry may be a result of Quantum effects

11. Wave Mechanics - Any randomness and asymmetry is as a result of Unknown Forces


Domain Scope / Scale Randomness Pioneers

Classical Mechanics

(Newtonian Physics)

Everyday objects Any apparent randomness is as

a result of Unknown Forces

Sir Isaac Newton

Thermodynamics Entropy, Enthalpy Newcomen, Trevithick,

Watt, Stephenson

Biology Evolution Darwin, Banks, Huxley,

Krebs, Crick, Watson

Chemistry Molecules Lavoisier, Priestley

Atomic Theory Atoms Each and every Quantum event

is truly and intrinsically fully

symmetrical and random

Max Plank, Niels Bohr

Quantum Mechanics Sub-atomic particles Erwin Schrodinger ,

Werner Heisenberg,

Paul Dirac,

Richard Feynman



Geology The Earth, Planets,

Planetoids, Asteroids,

Meteors / Meteorites

Any apparent randomness is as


Hutton, Lyell, Wagner

Astronomy Common, Observable

Celestial Objects

Any apparent randomness or

asymmetry may be as a result

of Quantum effects or other

Unknown Forces acting early in

the history of Space-Time

Galileo, Copernicus,

Kepler, Lovell, Hubble

Cosmology Super-massive

Celestial Objects

Hoyle, Ryall, Rees,

Penrose, Bell-Burnell

Relativity Theory The Universe


asymmetry is as a result of

Unknown Forces / Dimensions

Albert Einstein,

Hermann Minkowski,

Stephen Hawking

Wave Mechanics

(String Theory or

Quantum Dynamics)

The Universe,

Membranes and

Hyperspace

Michael Green,

Michio Kaku


• Classical Mechanics (Newtonian Physics)

– Classical Mechanics (Newtonian Physics) governs the behaviour of everyday objects

– any apparent randomness is as a result of unimaginably small, unobservable and

unmeasurable Unknown Forces - either internal or external - acting upon a System.

• Thermodynamics

– governs the flow of energy and the transformation (change in state) of systems

– randomness, chaos and uncertainty is the result of the effects of Enthalpy and Entropy

• Chemistry

– Chemistry (Transformation) governs the change in state of atoms and molecules



• Biology

– Biology (Ecology ) governs Evolution - the life and death of all living Organisms




• Atomic Theory

– governs the behaviour of unimaginably small objects (atoms and sub-atomic particles)

– all events are truly and intrinsically, utterly and unerringly predictable (Dirac Equation).

• Quantum Mechanics

– governs the behaviour of unimaginably tiny objects (fundamental sub-atomic particles)

– all events are truly and intrinsically both symmetrical and random (Hawking Paradox).

• Geology

– Geology governs the behaviour of local Solar System Objects (such as The Earth, Planets,

Planetoids, Asteroids, Meteors / Meteorites) which populate the Solar System


unmeasurable Unknown Forces - either internal or external - acting upon a System

• Astronomy

– Astronomy governs the behaviour of Common, Observable Celestial Objects (such as

Asteroids, Planets, Stars and Stellar Clusters) which populate and structure Galaxies

– any apparent randomness or asymmetry is as a result of Quantum Effects, Unknown

Forces or Unknown Dimensions acting very early in the history of Universal Space-Time


• Cosmology

– Cosmology governs the behaviour of impossibly super-massive cosmic building blocks

(such as Galaxies and Galactic Clusters) which populate and structure the Universe

– any apparent randomness or asymmetry is due to the influence of Quantum Effects,

Unknown Forces (Dark Matter, Dark Flow and Dark Energy) or Unknown Dimensions

• Relativity Theory

– Relativity Theory governs the behaviour of impossibly super-massive cosmic structures




• Wave Mechanics (String Theory or Quantum Dynamics)

– Wave Mechanics integrates the behaviour of every size and type of physical object


Forces or Unknown Dimensions acting on the Universe, Membranes or in Hyperspace


• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration of

Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic) context.

The problems encountered in exploring and analysing vast volumes of spatial–temporal

information in today's data-rich landscape – are becoming increasingly difficult to manage

effectively. In order to overcome the problem of data volume and scale in a Time (history) and

Space (location) context requires not only traditional location–space and attribute–space

analysis common in GIS Mapping and Spatial Analysis - but now with the additional dimension

of time–space analysis. The Temporal Wave supports a new method of Visual Exploration for

Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-visual

“Big Data” analytics. The temporal wave combines the strengths of both linear timeline and

cyclical wave-form analysis – and is able to represent data both within a Time (history) and

Space (geographic) context simultaneously – and even at different levels of granularity. Linear

and cyclic trends in space-time data may be represented in combination with other graphic

representations typical for location–space and attribute–space data-types. The Temporal Wave

can be used in roles as a time–space data reference system, as a time–space continuum

representation tool, and as time–space interaction tool.


• Randomness. Neither data-driven nor model-driven macro-economic or micro-economic

models currently available to us today - seem able to deal with the concept or impact of

Random Events (uncertainty). We therefore need to consider and factor in further novel

and disruptive (systemic) approaches which offer us the possibility to manage uncertainty.

We can do this by searching for, detecting and identifying Weak Signals – which are tiny,

unexpected variations or disturbances in system outputs – surprises – predicating the

possible existence of hidden data relationships which are masked or concealed within the

general background system “noise”. Weak Signals are caused by the presence of small

unrecognised or unknown forces acting on the system. Weak Signals in turn may indicate

the possible future appearance of emerging chaotic, and radically disruptive Wild Card or

Black Swan events beginning to form on the detectable Horizon – or even just beyond.

• Random Events must then be factored into Complex Systems Modelling. Complex

Systems interact with unseen forces – which in turn act to inject disorder, randomness,

uncertainty, chaos and disruption. The Global Economy, and other Complex Adaptive

Systems, may in future be considered and modelled successfully as a very large set of

multiple interacting Ordered (Constrained) Complex Systems - each individual System

loosely coupled with all of the others, and every System with its own clear set of rules and

an ordered (restricted) number of elements and classes, relationships and types.

Future Management Research Philosophy

“Research philosophy is an over-arching term relating to the

development of knowledge - and understanding the nature of that

knowledge which is under development.....”

• Adapted from Saunders et al, (2009) •

Epistemology concerns the scope of what constitutes acceptable knowledge in a field of study.

Ontology is concerned with the nature of reality - and raises questions about assumptions

Thinking about the Future

Research Philosophies

• This section aims to discuss Risk Research Philosophies in detail, in order to develop

a general awareness and understanding of the options - and to describe a rigorous

approach to Research Methods and Scope as a mandatory precursor to the full Risk

Research Design. Kvale (1996) and Denzin and Lincoln (2003) highlight how different

Research Philosophies can result in much tension amongst research stakeholders.

• When undertaking any research of either a Scientific or Humanistic nature, it is most

important to consider, compare and contrast all of the varied and diverse Research

Philosophies and Paradigms that are available to the researcher and supervisor -

along with their respective treatments of ontology and epistemology issues.

• Since Research Philosophies and paradigms often describe dogma, perceptions,

beliefs and assumptions about the nature of reality and truth (and knowledge of that

reality) - they can radically influence the way in which the research is undertaken,

from design through to outcomes and conclusions. It is important to understand and

discuss these contrasting aspects in order that approaches congruent to the nature

and aims of the particular study or inquiry in question, are adopted - and to ensure

that researcher and supervisor biases are understood, exposed, and mitigated.


• James and Vinnicombe (2002) caution that we all have our own inherent preferences

that are likely to shape our research designs and conclusions, Blaikie (2000) describes

these aspects as part of a series of choices that the researcher has to consider, and

demonstrates that this alignment that must connect choices made back to the original

Research Problem. If this is not achieved, then certain research methods may be

adopted which turn out to be incompatible with the researcher’s stance, and result in

the final work being undermined through lack of coherence and consistency.

• Blaikie (1993) argues that Research Methods aligned to the original Research Problem

are highly relevant to Social Science since the humanistic element introduces a

component of “free will”’ that adds a complexity beyond those usually encountered in

the natural sciences – whilst others, such as Hatch and Cunliffe (2006) draw attention

to the fact that different paradigms ‘encourage researchers to study phenomena in

different ways’, going on to describe a number of organisational phenomena from three

different perspectives, thus highlighting how different kinds of knowledge may be

derived through observing the same phenomena from different philosophical

viewpoints and perspectives.

Aspects of Research Philosophy

• Rationalism – “blue-sky” pure research - the stance of the natural scientist – Rationalism can be defined as “probabilistic research approaches that employ forensic and

analytical methods, make extensive use of both qualitative and quantitative analysis - free from any pre-determined behavioral models - in order to discover hidden or unknown truths”

• Positivism – goal seeking - the stance of the applied scientist – Positivism can be defined as “deterministic research approaches that employ empirical

methods, and make extensive use of quantitative analysis, or develop logical calculi in order to develop hypotheses and build conceptual models in support of formal explanatory theory”

• Realism – direct and critical realism – The essence of realism is that what the senses show us as reality is the truth; that objects

have an existence independent of the human mind.

• Interpretation – researchers as ‘social actors’ – Interpretation advocates the necessity for researchers to understand differences between

humans in our role as social actors.

• Pragmatism – studies judgements about value – Pragmatism holds that the most important determinant of the epistemology, ontology,

axiology adopted is the research question

Saunders et al, (2009)

Probabilistic v. Deterministic Domains Deterministic

Probabilistic Rationalism

Positivism Gnosticism, Sophism

Scepticism

Dogma

Enlightenment

Pragmatism

Realism

Social Sciences

Sociology

Economics

Business Studies / Administration / Strategy

Psychology / Psychiatry / Medicine / Surgery

Behavioural Research Domains

Arts and the Humanities

Life Sciences

History Arts Literature Religion

Law Philosophy Politics

Biological basis of Behaviour

Biology Ecology Anthropology and Pre-history

Clinical Trials / Morbidity / Actuarial Science

“Goal-seeking” Empirical Research Domains

Applied (Experimental) Science

Earth Sciences

Economic Analysis

Classical Mechanics (Newtonian Physics)

Applied mathematics

Geography

Geology

Chemistry

Engineering

Geo-physics Environmental Sciences

Archaeology

Palaeontology

“Blue Sky” – Pure Research Domains

Future Management

Pure (Theoretical) Science

Quantitative Analysis

Computational Theory / Information Theory

Astronomy

Cosmology

Relativity

Astrophysics

Astrology

Taxonomy and Classification

Climate Change

Complex and Chaotic Research Domains

Narrative (Interpretive) Science

Statistics

Strategic Foresight

Data Mining “Big Data” Analytics

Cluster Theory

Pure mathematics

Particle Physics

String Theory

Quantum Mechanics

Complex Systems – Chaos Theory

Futures Studies

Weather Forecasting Predictive Analytics

Reaction

Stoicism

DETERMINISTIC PHILOSOPHIC PARADIGMS

GOVERNING AUTHORITIES tend to be Deterministic in nature - 12 Deterministic Paradigms.....

DETERMINISTIC PHILOSOPHIC PARADIGMS

• Utopian (Idealistic) Paradigm - Strategic Positivism

• Humanist (Instructional) Paradigm - Sceptic Paradigm

• Dogmatic (Theosophical) Paradigm - Reactionary Paradigm

• Utilitarian (Consequential) Paradigm – Egalitarian Paradigm

• Extrapolative (Projectionist) Paradigm – Wave, Cycle, Pattern and Trend Analysis

• Steady State (La meme chose - same as it ever was) Paradigm – Constant Paradigm

• Hellenistic (Classical) Paradigm – Human Ethics, Morals, Values and Beliefs

• Pre-ordained (Pre-disposed, Stoic) Paradigm - Cognitive Analysis / Intuitive Assimilation

• Elitism (New World Order) - Goal Seeking, Leadership Studies and Stakeholder Analysis

• Existentialist Paradigm (Personal Futures) - Trans-humanism, The Singularity, NLP / EHT

• Empirical (Scientific Determinism, Theoretical Positivism) Paradigm – Hypothetical Paradigm

• Predictive (Ordered, Systemic, Mechanistic, Enthalpy) Paradigm – Deconstructive Paradigm

PROBABILISTIC PHILOSOPHIC PARADIGMS

NATURAL PHILOSOPHERS tend to be Probabilistic in nature - 12 Probabilistic Paradigms.....

PROBABILISTIC PHILOSOPHIC PARADIGMS

• Polemic (Rational) Paradigm - Enlightenment

• Dystopian (Fatalistic) Paradigm – Probabilistic Negativism

• Postmodernism (Reactionary) Paradigm - Structural Philosophy

• Complexity (Constructionist) Paradigm - Complex Systems and Chaos Theory

• Metaphysical (Naturalistic, Evolutionary, Adaptive) Paradigm - Gaia Hypothesis

• Mystic (Gnostic, Sophistic, Esoteric, Cathartic) Paradigm – Contemplative Paradigm

• Uncertainty (Random, Chaotic, Disorderly, Enthalpy) Paradigm – Disruptive Paradigm

• Experiential (Forensic, Deductive, Realist, “Blue Sky”) Paradigm – Pragmatism

• Qualitative (Narrative, Reasoned) Paradigm - Scenario Forecasting and Impact Analysis

• Simplexity (Reductionist) Paradigm – Loosely-coupled Linear Systems and Game Theory

• Interpretive (Ordered, Systemic, Mechanistic, Entropic) Paradigm – Constructive Paradigm

• Quantitative (Logical, Technical) Paradigm - Mathematical Modelling & Statistical Analysis

Ancient Philosophy

• 1 Ancient Chinese philosophy

– 2.1 Schools of thought

• 2.1.1 Hundred Schools of Thought

• 2.1.2 Early Imperial China

– 2.2 Philosophers

• 2 Ancient Greek and Roman philosophy

– 3.1 Philosophers

• 3.1.1 Presocratic philosophers

• 3.1.2 Classical Greek philosophers

• 3.1.3 Hellenistic philosophy

– 3.2 Hellenistic schools of thought

– 3.3 Early Roman and Christian philosophy

– 3.4 Philosophers during Roman times

http://en.wikipedia.org/wiki/Ancient_philosophy










































Ancient Philosophy

• 4 Ancient Indian philosophy

– 4.1 Vedic philosophy

– 4.2 Sramana philosophy

– 4.3 Classical Indian philosophy

– 4.4 Ancient Indian philosophers

• 4.4.1 Philosophers of Vedic Age (2000–600 BCE)

• 4.4.2 Philosophers of Axial Age (600–185 BCE)

• 4.4.3 Philosophers of Golden Age (184 BCE – 600 CE)

• 5 Ancient Iranian philosophy

– 5.1 Schools of thought

– 5.2 Philosophy and the empire

– 5.3 Literature






















































Ancient Philosophy

• 6 Ancient Jewish philosophy

– 6.1 First Temple (c. 900 BCE to 587 BCE)

– 6.2 Assyrian exile (587 BCE to 516 BCE)

– 6.3 Second Temple (516 BCE to 70 CE)

– 6.4 Early Roman exile (70 CE to c. 600 CE)
















Hellenistic Schools of Thought

• 1 Pre-Socratic philosophy

– 1.1 Milesian school

– 1.2 Xenophanes

– 1.3 Pythagoreanism

– 1.4 Heraclitus

– 1.5 Eleatic philosophy

– 1.6 Pluralism and atomism

– 1.7 Sophistry

• 2 Classical Greek philosophy

– 2.1 Socrates

– 2.2 Plato

– 2.3 Aristotle

• 3 Hellenistic philosophy

• 4 Transmission of Greek philosophy under Islam

http://en.wikipedia.org/wiki/Ancient_Greek_philosophy
















































Hellenistic Schools of Thought

1. Pythagoreanism

2. Sophism

3. Cynicism

4. Cyrenaicism

5. Platonism

6. Peripateticism

7. Pyrrhonism

8. Epicureanism

9. Stoicism

10. Eclecticism

11. Hellenistic Judaism

12. Neopythagoreanism

13. Hellenistic Christianity

14. Neoplatonism

Hellenistic schools of Philosophy and Thought

http://en.wikipedia.org/wiki/Hellenistic_philosophy















Philosophical Paradigms Mystic (Hermetic) Philosophy

Description Leading figures

mystic (hermetic)

philosophy

Related: -

Transcendentalism;

Perennial Philosophy;

Unitarian Universalism;

Gnosticism and the Kabala.

Mystic (Hermetic) Philosophy in which “secret”

or “hidden” knowledge is revealed to initiates in a

series of steps or “degrees”, in ritual practices or

religious rites based on Perennial Philosophy;

Transcendentalism and Unitarian Universalism

– are found in Jewish and Christian Gnosticism,

the Kabala and Moslem Sufism. The essence of

divination in mystic (hermetic) philosophy is the

belief that wisdom, knowledge or “spiritual truth”

can be gained through contemplative meditation –

trance-like deep thinking or prayer – in a state of

mystical union with the universal force, or in direct

communion with the supreme spiritual being – as

is described by mystic (hermetic) philosophers.

The Kybalion Hermetic Philosophy was published

anonymously in 1908 by an individual or group of

individuals under the pseudonym of "the Three

Initiates" – which they claimed to be the essential

distillation of the mystic philosophical teachings of

Hermes Trismegistus the “Thrice-Great” - which

is interpreted today as associated with learning or

wisdom transmitted to man from divine sources.

Hermes Trismegistus, Philo of

Alexandria, Flavius Josephus,

Michel Nostradamus.

Hermes Trismegistus. the “Thrice-

Great”. Hermes is interpreted as a

figure associated with learning or

wisdom transmitted to man from

divine sources. Hermes is the Greek

name for the Egyptian god Thoth or

Tehuti, the god of wisdom, learning

Philo of Alexandria, also called

Philo Judaeus, was a Hellenistic

Jewish philosopher who lived in

Alexandria, in the Roman province of

Egypt. Philo used philosophical

allegory to attempt to fuse and

harmonize Greek Stoic philosophy

with Jewish Kabala philosophy.

http://www.merriam-webster.com/dictionary/mystical

http://www.merriam-webster.com/dictionary/mystics

Philosophical Paradigms Mystic (Hermetic) Philosophy


mystic (hermetic)

philosophy

Related: -

Transcendentalism;

Perennial Philosophy;

Unitarian Universalism;

Gnosticism and the Kabala.

Josephus was a priest, soldier, and scholar. He is

famous for his prophecies – and also for being the

most credible secular historian outside of the New

Testament to record the existence of Jesus Christ.

Prophetic visions of Nostradamus are contained

in 942 cryptic poems called The Centuries.

Nostradamus wrote four-line verses (quatrains) in

groups of 100 (centuries). They have enthralled

generation after generation of readers. He was

often referred to as the prophet of doom because

many of his visions involved conflict and death.

Titus Flavius Josephus, born

Joseph ben Matityahu, was a first-

century Romano-Jewish scholar,

historian and philosopher, born in

Jerusalem - part of Roman Judea -

to a father of priestly descent and a

mother who claimed royal ancestry

Michel de Nostredame, usually

Latinised as Nostradamus (14 or 21

December 1503 – 2 July 1566) was

from a Sephardic Jewish family who

had converted to Christianity. Born

in South-west France, the French

apothecary and reputed seer who

published “The Centuries” – a

collection of prophecies that have

since become famous worldwide.

http://www.sacred-texts.com/nos/index.htm

http://www.sacred-texts.com/nos/index.htm

Philosophical Paradigms Stoic Philosophy


Stoic philosophy

Related: -

Stoicism and

Rationalism

“Throughout eternity,

all that is of like form

will come around

again – everything

that is the same must

always return in its

own everlasting

cycle.....”

“Look back over time,

with past empires

that in their turn rise

and fall – through

changing history you

may also see the

future.....”

Stoicism is a school of Hellenistic philosophy

founded in Athens by Zeno of Citium in the early 3rd

century BC. Stoic Philosophers taught that errors in

judgment resulted from destructive emotions, and that

a sage, or person of "moral and intellectual

perfection", would be entirely detached from any such

emotional impediments – and would therefore view

the world from an entirely Rational perspective..

Marcus Aurelius followed • Stoic Philosophy •

The Roman emperor Marcus Aurelius was perhaps

the only true philosopher-king in world history.

His Stoic tome Meditations, written in Greek while on

campaign between 170 and 180, is still revered as a

literary monument to a Stoic philosophy of service

and duty, describing how to find and maintain a

rational equanimity in the midst of conflict by studying

and understanding nature as a source of instruction,

guidance and inspiration.

Lucius Anna of accelerationeus

Seneca (c. 4 BC-65 AD), Statesman,

Stoic philosopher, and playwright

Rational Futurists view the world

from an entirely objective and

Rational perspective – devoid of any

subjective or emotional influences

• Marcus Aurelius • Emperor of Rome

Marcus Aurelius Latin: Marcus

Aurelius Antoninus Augustus; 26 April

121 AD –17 March 180 AD) was

chosen by Hadrian to be his eventual

successor. He was Roman Emperor

from 161 to 180, he ruled jointly with

Lucius Verus as his co-emperor from

161 until Verus' death in 169. He was

the last of the Five Good Emperors,

and is also considered one of the

most important Stoic philosophers.

http://en.wikipedia.org/wiki/Lucius_Annaeus_Seneca




http://en.wikipedia.org/wiki/Latin_language

http://en.wikipedia.org/wiki/Roman_Emperor

http://en.wikipedia.org/wiki/Lucius_Verus

http://en.wikipedia.org/wiki/Lucius_Verus

http://en.wikipedia.org/wiki/Nerva%E2%80%93Antonine_dynasty#Five_Good_Emperors

http://en.wikipedia.org/wiki/Stoicism

Philosophical Paradigms Dystopian Philosophy


Dystopian

philosophy

Dystopia: A futuristic, imaginary society, world or

universe that exerts oppressive citizen control - in

which a figurehead or concept is worshipped by

the unfortunate citizens of that dystopian society.

The prologue to Book I of Milton Paradise Lost

begins with Milton stating that his subject will be

Adam and Eve’s disobedience and fall from grace

in order to discourse about the fall of man and to

justify God's harsh ways of dealing with mankind.

Brave New World is a novel written in 1931 by

Aldous Huxley and published in 1932. Set in a

future London of AD 2540, the novel anticipated

the rise of Fascism in Europe – developments in

state-sponsored propaganda and terrorism, the

technology of warfare, centralised control of

sexual and reproductive functions, euthanasia,

psychological profiling and sleep-learning

The Dystopian view or viewpoint

In Futures Studies is based on

the upward trend in developing

global crises which threaten the

future of mankind – war, terrorism,

rebellion, insecurity and civil

disobedience, Climate Change,

Population Growth and the rapid

exhaustion of natural resources -

Food Energy and Water

John Milton. (1608–1674)

Paradise Lost - where demons

released to live in Pandemonium

wander – to prey on lost souls.

Brave New World is an unsettling,

loveless and even sinister place.

This is because Huxley endows his

"ideal" society with features and

characteristics calculated to

alienate his readers from this so-

called “Utopia”.

Philosophical Paradigms Utopian Philosophy


Utopian

philosophy

Utopian philosophy – Utopia is an imagined

place or state of affairs in which everything is

perfect. The word Utopia is constructed from two

Greek words - TOPOS meaning PLACE and OU

meaning NO. Thus Utopia is "nowhere" or an

imaginary place. It is also a pun on the word EU

meaning good or perfect. So Utopia can also be a

seen as a perfect place - which is non-existent.

Sir Thomas Moore published Utopia in 1516 and

since then the word Utopia has become the

generic name for a whole genre of speculative

writing, philosophy and ideology - also being

retrospectively applied to works like Plato's

Republic, upon which Utopia is largely based.

Milton's last two poems were published in one

volume in 1671. Paradise Regained, a brief epic

in four books, was followed by Samson Agonistes

In Futures Studies, a Utopian

view or idealistic viewpoint is

founded on the false hope or

promise that future of mankind will

sort itself out, and everything is

going to be all right…..

Sir Thomas More (later canonized

St. Thomas a’ More) is famous for

both his book Utopia (1515) and

for his Catholic martyrdom when

he refused to acknowledge King

Henry VIII as head of the

Protestant Church, then failed to

sanction Henrys’ divorce of Queen

Catherine of Aragon.

John Milton. (1608–1674)

Paradise Regained

Philosophical Paradigms Philosophy Description Leading figures Polemic philosophy Polemic comes from the Greek polemikos meaning

"warlike” or “belligerent” - a philosophy based on

confrontational argument or adversarial debate –

particularly when attacking an opposing doctrine or

refuting an opposite view, viewpoint or opinion - often

accompanied by controversial, misleading,

contentious or spurious statements.

Nietzsche and Voltaire are examples

of Polemic Philosophers.

Polemic Futures Studies is based

on the forceful arguments put forward

by the opponents and detractors of

Darwinian Evolution, Climate Change

Romantic

Philosophy –

Related: -

Epistemology

Romanticism

Natural Philosophy

Romantic Movement

The Romantic Movement emerged out of the

Romantic challenge to both the static, materialistic

views of the Enlightenment and its diametrically

opposed Idealistic Paradigm – so developing as a

reaction against their contrary methods, validity, and

scope – in respect of both their ethical values, and the

distinction between “opinion” and “justified belief”.

Romantic Philosophy, along with its new

epistemology of nature (natural history – the study of

life) came to be the prevailing mood of the 18th

century – a popular movement which also sparked

the religious revival to which both the Evangelical and

High Church movements bear stark witness

(Epistemology – the theory of knowledge)

Romantic movement – an artistic,

literary, and intellectual movement

emerging in Western Europe during

the second half of the 18th century: -

http://www.visual-arts-cork.com/history-of-art/romanticism.htm

Philosophical Paradigms Metaphysical Philosophy


metaphysical

philosophy

In Western philosophy,

metaphysics has become

the study of the

fundamental nature of

reality - what it is, why it

is, and how we are to able

to understand its true

properties.....

Metaphysics is a broad area of philosophical

enquiry marked out by two types of question.

The first question aims to be the most general

investigation possible into the nature of reality: -

are there principles which apply to all that is real,

to everything that exists? Through abstraction

from the particular (specific) nature of existing

things - what distinguishes them from each other,

what can we learn generically about objects

merely by virtue of the fact that they exist?

The second question or type of inquiry seeks to

uncover what is ultimately real - frequently

offering answers in sharp contrast to our

everyday experience of the world. Through an

understanding of the terms of these two

paradigms, metaphysics is very closely related to

ontology - which is usually taken to involve both

‘what is existence (being)?’ and ‘what types of

(fundamentally distinct) objects exist?’

Emmanuel Kant, Carl Linaeus,

Charles Darwin, Thomas Huxley

Thomas Henry Huxley was one of

the first adherents to Darwin's theory

of evolution by natural selection, and

did more than anyone else to

advance its claims – including a

famous debate in 1860 with Bishop

Samuel Wilberforce – this was a key

moment in his own career and in the

wider acceptance of evolution.



metaphysical

philosophy

Related: -

Alchemy

Ontology

Taxonomy

Classification

Natural History

Natural Philosophy

Systemic

Methodology

Rationalism can be defined as “probabilistic research

approaches that employ forensic and analytical

methods, make extensive use of both qualitative and

quantitative analysis - free from any pre-determined

behavioural models - in order to discover the secrets

of hidden or “unknown” truths

Positivism can be defined as “deterministic research

approaches that employ empirical methods, and

make extensive use of quantitative analysis, or

develop logical calculi in order to develop hypotheses

and build conceptual models in support of formal

explanatory theory”

The essence of Realism is that what the senses

show us as reality is the truth; that objects have an

existence independent of the human mind.

Interpretation advocates the necessity for

researchers to understand differences between


Pragmatism holds that the most important

determinant of the epistemology, ontology, axiology

adopted is the question posed by the research

Rationalism – “blue-sky” research -

the natural stance of the free and

unencumbered “pure” scientist

Positivism – goal seeking - the

natural stance of the restricted and

constrained “applied” scientist

Realism – the direct, critical and

objective science of realism

Interpretation – scientific

researchers as “social actors”

Pragmatism – studies subjective

judgements about questions of

ethics, values and beliefs



metaphysical

philosophy

In Western philosophy,

metaphysics has

become the study of the

fundamental nature of

reality - what it is, why it

is, and how we are to

understand its

properties.....

Related: -

Alchemy

Ontology

Taxonomy

Classification

Natural History

Natural Philosophy

Systemic Methodology

Darwinism is a theory of biological evolution

which was developed by Charles Darwin and

others, stating that all species of organisms arise

and develop over time through the mechanism of

natural selection – in which only those organisms

best adapted to their environment survive long

enough to reproduce – and therefore pass on their

genetic information to the next generation.

Relativity or the theory of relativity in physics,

encompasses two theories by Albert Einstein: the

case of special relativity and general relativity –

each describe the properties of Mass and Energy

(Mass-Energy) interacting with Space and Time.

The Minkowski Space-Time continuum is a four-

dimensional manifold or construct, described by

Hermann Minkowski to better understand the case

of special relativity. It has four dimensions - three

dimensions of space inextricably linked with time.

Hubble's Law is named after astronomer Edwin

Hubble whose studies of distant galaxies gave us

an understanding of the expanding Universe.

Edwin Hubble was hired to work at

Mount Wilson Observatory in 1919

(part of the Observatories of the

Carnegie Institution of Washington)

as a junior astronomer. During the

1920's and 30's, Edwin Hubble

discovered that the Universe is

expanding, with galaxies moving

away from each other at a rapidly

increasing rate of acceleration



metaphysical

philosophy

In Western

philosophy,

metaphysics has

become the study of

the fundamental

nature of reality -

what it is, why it is,

and how we are to

understand its

properties.....

Related: -

Alchemy

Ontology

Taxonomy

Classification

Natural History

Natural Philosophy

Systemic

Methodology

String theory is a set of mathematical attempts to

model the four known fundamental interactions -

gravitation, electromagnetism, strong nuclear force,

weak nuclear interaction – clustered into a single,

universal Theory of Wave Dynamics.

In theoretical physics, M-theory is an extension of

string theory in which the 11 dimensions of space-

time are identified as 7 higher-dimensions plus the 4

common dimensions (11D st = 7 hd + 4D).

Proponents believe that the 11-dimensional theory

unites all five 10-dimensional string theories (10D st =

6 hd + 4D) and supersedes them. Though a full

description of the theory is not known, the low-entropy

dynamics are thought to be supergravity interacting

with 2- and 5-dimensional membranes in a single,

unified Theory of Wave Dynamics.

Gabriele Veneziano – is an Italian

theoretical physicist and string

theorist. His 1968 dual resonance

model of the strong interaction was

the first component of string theory to

be described - he is regarded as a

founder of this field of String Theory.

Brian Greene, author of the book

about string theory, The Elegant

Universe, was educated at Harvard

and Oxford, graduating in 1987. After

working at Harvard and Cornell, he is

currently a Professor of Physics and

Mathematics at Columbia.

http://en.wikipedia.org/wiki/Theoretical_physics

http://en.wikipedia.org/wiki/String_theory

http://en.wikipedia.org/wiki/Dimensions

http://en.wikipedia.org/wiki/Spacetime



http://en.wikipedia.org/wiki/Supergravity#11d:_the_maximal_SUGRA

http://en.wikipedia.org/wiki/Membrane_(M-Theory)



Pre-Raphaelite

Philosophy

Related: -

Gothic Revival

Pre-Raphaelite

Movement

Pre-Raphaelite

Brotherhood

The Pre-Raphaelite Movement (also known as the

Pre-Raphaelite Brotherhood or Pre-Raphaelites)

were a group of English painters, poets, and critics,

founded in 1848 by William Holman. Pre-

Raphaelitism sprang from a new mood in English

painting, reflecting the great moral and material

changes of the age which, by the middle of the 19th

century, saw the arrival of a new generation of artists

– who were forerunners of the Modern Art movement.

Major influences included the industrial revolution,

which brought far-reaching social changes - not least

important, a new wealthy Middle Class whose taste

and outlook were formed under new influences -

along with a renewed interest in nature and the arts

and culture of the Middle Ages - the Gothic revival....

The Pre-Raphaelite Brotherhood,

founded in 1848 by William Holman,

rejected post-Enlightenment arts and

society - looking back to the Middle

Ages for artistic / cultural inspiration.

Dante Gabriel Rossetti - John Everett

Millais - Quattrocento - William

Holman Hunt, Ford Madox Brown

Postmodern

philosophy

Postmodernism is a philosophical movement or

tendency which is critical of the fundamental

principles, assumptions and general direction which

underpin classical western philosophy.

Postmodernism emphasises the importance of

discourse, power relationships, personalisation and

individualism as key in the "construction" or

“rebuilding” of the Post-modernist world viewpoint.

Architecture features strongly in the

post-modernistic movement – Aldo

Rossi, Frank Gehry and Terry Farrell

http://www.visual-arts-cork.com/modern-art.htm

http://en.wikipedia.org/wiki/Dante_Gabriel_Rossetti

http://en.wikipedia.org/wiki/John_Everett_Millais

http://en.wikipedia.org/wiki/John_Everett_Millais

http://en.wikipedia.org/wiki/Quattrocento

http://en.wikipedia.org/wiki/William_Holman_Hunt

http://en.wikipedia.org/wiki/William_Holman_Hunt

Philosophical Paradigms Philosophy Description Leading figures Complexity

Paradigm

From the Paradigm (concept) of Complexity to

System Complexity - philosophical, scientific and

professional disciplines addressing complexity in

their fields all view the Complexity Paradigm as based

on the behaviour of complex systems and the rich

conceptual world of non-linear equations – centred on

the science of turbulence and chaos, strange

attractors, emergence and fractals, self-organisation

and critical system complexity.

Edward Lorenz, John Henry Holland,

Edgar Morin

Disruptive Futurism Disruptive Futurism is a Futurist Framework for

Digital Technology Disruption – Digital Platform and

Service convergence – which, since the year 2000,

has severely impacted on the performance of 52% of

the Fortune 500 listed companies.

Joseph Schumpeter – Austrian

School Economist

Ian Neild – BT Laboratories

Experiential

Philosophy (versus

Argumentative

Philosophy)

The New Philosophy:

Cognitive Science &

Experiential Realism

Experiential Philosophy maintains that real-world

experience has priority over theoretical

argumentation; as Lewis Carroll so brilliantly

demonstrated, even the most logical of arguments

cannot persuade someone who refuses to experience

the rationality of the move from some set of premises

to their logical conclusion

John Dewey - experiential training,

education and learning (1938)

Experiential education and training

refers to a pedagogical philosophy

and methodology concerned with

practical, hands-on learning activities

http://www.ditext.com/carroll/tortoise.html

http://www.ditext.com/carroll/tortoise.html



Interpretive

Paradigm

Interpretive research operates within a research

paradigm that functions differently from traditional

research in the humanities or social sciences - as it

operates without any prior assumptions or pre-

determined views about findings or outcomes.

Interpretive views have different originations in

different disciplines. Positivism (Deterministic) and

Phenomenological (Interpretive) research and the

growing consensus of a mixed methods approach to

research studies – explains a rapidly increasing

popularity in interpretative research methods.

Schultz, Cicourel and Garfinkel

Interpretive Research is used In

pure research studies, within a

research paradigm that functions

somewhat differently from traditional

research in applied science, the

humanities or social sciences - as it

proceeds without any fixed model,

prior assumptions or pre-determined

views about any of the findings or

outcomes of the research study.

Deterministic

Paradigms –

Positivism (and

Post-positivism)

Positivism is sometimes referred to as the 'scientific

method' or 'science-based research', and in

Management Studies, as ‘strategic positivism’.

Positivism as such is founded on a deterministic,

rationalistic, empiricist philosophy that originated with

Aristotle, Francis Bacon, John Locke, August Comte

and Emmanuel Kant" (Mertens, 2005, p.8) and

"reflects a deterministic philosophy in which causes

probabilistic pre-determined outcomes and effects"

(Creswell, 2003, p.7).

Circa Trova – seek and ye shall find.

Aristotle, Francis Bacon, John Locke,

August Comte and Emmanuel Kant

The scientific researcher observes

the behaviour of a system, formulates

a hypothesis to explain the observed

behaviour, and then designs and

executes an experiment to test how

well his hypothesis predicts the actual

and real observations and outcomes.

Philosophical Paradigms Philosophy Description Leading figures Qualitative

Paradigm

Qualitative Methods

- tend to be

deterministic,

interpretive and

subjective in

nature.

When we wish to design a research project to

investigate large volumes of unstructured data

producing and analysing graphical image and text

data sets with a very large sample or set of

information – “Big Data” – then the quantitative

method is preferred. As soon as subjectivity - what

people think or feel about the world - enters into the

scope (e.g. discovering Market Sentiment via Social

Media postings), then the adoption of a qualitative

research method is vital. If your aim is to understand

and interpret people’s subjective experience and the

broad range of meanings that attach to it, then

interviewing, observation and surveying a range of

non-numerical data (which may be textual, visual,

aural) are key strategies you will consider. Research

approaches such as using focus groups, producing

case studies, undertaking narrative or content

analysis, participant observation and ethnographic

research are all important qualitative methods. You

will also want to understand the relationship of

qualitative data to numerical research. Any qualitative

methods pose their own problems with ensuring the

research produces valid and reliable results (see also:

Data Science and working with “Big Data” Analytics.

Qualitative Paradigm. Most

qualitative research texts identify

three primary types of research:-

1. Exploratory – research on a

concept, people, or situation that

the researcher knows little about.

2. Descriptive (Narrative) –

research on a concept, people,

process or situation that the

researcher knows something

about, but just wants to describe

the narrative findings that he/she

has found or observed.

3. Explanatory – involves deriving

a hypothesis from existing

theories and available models,

then testing that hypothesis

through a process of

experimental observation and

data collection.

Philosophical Paradigms Philosophy Description Leading figures Quantitative

Paradigm

Quantitative

Methods - tend to

be probabilistic,

analytic and

objective in nature.

When we want to design a research project to test a

hypothesis objectively by capturing and analysing

numerical data sets with a large sample or set of

information – then the quantitative method is

preferred. There are many key issues to consider

when you are designing an experiment , predictive

model, system or some other research project using

quantitative methods - such as randomisation,

selection and sampling.. Also, quantitative research

uses mathematical and statistical means extensively

to produce reliable analysis of its results (see also:

Cluster Analysis and Wave-form Analysis methods).

Quantitative Research refers to the

systematic empirical investigation of

social and scientific phenomena

through system modelling and

statistical analysis - via direct

observation and careful collection of

mathematical, numerical or biometric

datasets, and thorough analysis and

interpretation of the data.

Scientific Research observes and

collects data on the behaviour of a

system, formulates a hypothesis to

explain the observed behaviour, and

then designs and executes an

experiment to test how well his

hypothesis predicts the actual and

real observations and outcomes.

Future Taxonomy

There are some 10-20 Primary Futures Disciplines, 20-30 Futures Paradigms and over 250 Secondary Futures

Specialities documented in various sources – covering Futures Studies, Strategic Foresight, Military and Business Strategy, Economic Modelling and Long-range Forecasting,

Business Planning and Financial Analysis –

Future Taxonomy

• The main objective of any Futures Taxonomy is to identify, capture, analyse and classify the mainstream Futures Studies, Strategic Foresight and Strategy Analysis Primary Future Disciplines (20-30) Futures Studies Subjects (20-30) – Regimes, Frameworks and Paradigms, and then to document the Secondary Future Specialties (over 250) – Models, Methods, Tools and Techniques – and to order, group, define and describe both the Primary and Secondary subjects in a comprehensive, consistent, coherent, complete and logical manner.

• This is the first step towards creating a Futures Body of Knowledge (BOK)

• There are some 10-20 Primary Futures Disciplines, 20-30 Futures Paradigms and over 250 Secondary Specialities documented in various sources – covering Futures Studies, Strategic Foresight, Military and Business Strategy, Economic Modelling and Long-range Forecasting, Business Planning and Financial Analysis

• Primary Future Disciplines – 10-20 • Futures Studies Regimes, Frameworks and Paradigms – 20-30 • Secondary Future Specialties – up to 250

Probabilistic Future Viewpoints

• Polemic (Rational) Paradigm - Enlightened Futurism


• Postmodernism (Reactionary) Paradigm - Structural Futurism



• Mystic (Gnostic, Sophistic, Esoteric, Cathartic) Paradigm – Contemplative Futurism

• Uncertainty (Random, Chaotic, Disorderly, Enthalpy) Paradigm - Disruptive Futurism

• Experiential (Forensic, Deductive, Realist, “Blue Sky”) Paradigm – Pragmatic Futurism



• Interpretive (Ordered, Systemic, Mechanistic, Entropic) Paradigm – Constructive Futurism


Deterministic Future Viewpoints


• Humanist (Instructional) Paradigm - Sceptic Futurism

• Dogmatic (Theosophical) Paradigm - Reactionary Futurism

• Utilitarian (Consequential) Paradigm – Egalitarian Futurism


• Steady State (La meme chose - same as it ever was) Paradigm – Constant Futurism

• Hellenistic (Classical) Paradigm – Future of Human Ethics, Morals, Values and Beliefs




• Empirical (Scientific Determinism, Theoretical Positivism) Paradigm – Hypothetical Futurism

• Predictive (Ordered, Systemic, Mechanistic, Enthalpy) Paradigm – Deconstructionist Futurism

Primary Futures Research Disciplines

• Futures Studies

– History and Analysis of Prediction

– Future Studies – Classification and Taxonomy

– Future Management Primary Disciplines

– Future Management Secondary Specialisations

• Strategic Foresight

– Foresight Regimes, Frameworks and Paradigms

– Foresight Models, Methods, Tools and Techniques

• Qualitative Techniques

• Quantitative Techniques

• Systems Theory - Complexity

• Chaos Theory – Random Events, Uncertainty and Disruption

• Political and Economic Futures

• Science and Technology Futures

• Entrepreneurship and Innovation Futures

• Personal Futures – Trans-humanism, NLP / EHT

• The Future of Philosophy, Knowledge and Values

• Future Beliefs – Moral, Ethical and Religious Futures

• Massive Change – Human Impact and Global Transformation

• Human Futures – Sociology, Anthropology and Cultural Studies

• The Future of Information, Knowledge Management and Decision Support

Primary Futures Disciplines

Primary

Futures

Disciplines

9.

Future of Philosophy,

Knowledge & Values

7 .

Future of Information &

Knowledge Management

10. Future Beliefs –

Moral, Ethical

& Religious Futures

1. Futures Studies

4.

Science and

Technology Futures

12. Human Futures –

Sociology, Anthropology

and Cultural Studies

3.

Political & Economic

Futures

6.

Entrepreneurship &

Innovation Futures

2. Strategic Foresight

5.

Environment, Climate &

Ecology Futures

8.

Personal Futures –

Trans-humanism

11. Massive Change –

Human Impact and

Global Transformation


• Futures Studies – History and Analysis of Prediction – Future Studies – Classification and Taxonomy – Future Management Primary Disciplines – Future Management Secondary Specialisations

• Strategic Foresight – Foresight Regimes, Frameworks and Paradigms – Foresight Models, Methods, Tools and Techniques

• Quantitative Techniques • Qualitative Techniques • Chaos Theory – Random Events, Uncertainty and Disruption

• Political and Economic Futures • Science and Technology Futures • Entrepreneurship and Innovation Futures • Personal Futures – Trans-humanism, NLP / EHT • The Future of Philosophy, Knowledge and Values • Future Beliefs – Moral, Ethical and Religious Futures • Massive Change – Human Impact and Global Transformation • Human Futures – Sociology, Anthropology and Cultural Studies • The Future of Information, Knowledge Management and Decision Support

Secondary Future Specialties

• Monte Carlo Simulation • Forecasting and Foresight • Back-casting and Back-sight • Causal Layered Analysis (CLA) • Complex Adaptive Systems (CAS) • Political Science and Policy Studies • Linear Systems and Game Theory • War-gaming and Lanchester Theory • Complex Systems and Chaos Theory • Integral Studies and Future Thinking • Critical and Evidence-Based Thinking • Predictive Surveys and Delphi Oracle • Visioning, Spontaneity and Creativity • Foresight, Intuition and Pre-cognition • Developmental & Accelerative Studies • Systems & Technology Trends Analysis • Scenario Planning and Impact Analysis • Collaboration, Facilitation & Mentoring

• Black Swan Events - Weak Signals, Wild Cards, Chaos, Uncertainty & Disruption

• Economic Modelling & Planning • Financial Planning and Analysis • Ethics of Emerging Technology Studies • Horizon Scanning, Tracking & Monitoring • Intellectual Property and Knowledge • Critical Futures and Creative Thinking • Emerging Issues and Technology Trends • Patterns, Trends & Extrapolation Analysis • Linear Systems & Random Interactions • Cross Impact Analysis and Factors of

Global Transformation and Change • Preferential Surveys / Polls and Market

Research, Analysis and Prediction • The Future of Religious Beliefs - Theology,

Divinity, Ritual, Ethics and Value Studies • Divination – Hermetic, Mystic, Esoteric

and Enlightened Spiritual Practices


• Science and Technology Futures • The Cosmology Revolution

– Dark Energy, Dark Mass – String Theory and the Nature of Matter

• SETI – The Search for Extra-Terrestrial Planetary Systems, Life and Intelligence

• Nano-Technology, Nuclear Physics and Quantum Mechanics

• The Energy Revolution - Nuclear Fusion Hydrolysis and Clean Energy

• Science and Society Futures – the Social Impact of Technology

• Smart Cities of the Future • The Information Revolution – Internet

Connectivity and the Future of the Always-on Digitally Connected Society

• Digital Connectivity, Smart Devices, the Smart Grid & Cloud Computing Futures

• Content Analysis (“Big Data”) – Data Set “mashing”, Data Mining & Analytics

• Earth and Life Sciences – the Future of Biology, Geology & Geographic Science

• Environmental Sustainability Studies – Climatology, Ecology and Geography

• Human Activity – Climate Change and Future Environmental Degradation – Desertification and De-forestation

• Human Populations - Profiling, Analysis, Streaming and Segmentation

• Human Futures - Population Drift and Urbanisation - Human Population Curves and Growth Limit Analysis

• The Future of Agriculture, Forestry, Fisheries, Agronomy & Food Production

• Terrain Mapping and Land Use – Future of Topology, Topography & Cartography

• Future Natural Landscape Planning, Environmental Modelling and Mapping

• Future Geographic Information Systems, Spatial Analysis & Sub-surface Modelling


• Macro-Economic and Financial Futures • Micro-Economic and Business Futures • Strategic Visioning – Possible, Probable &

Alternative Futures • Strategy Design – Vision, Mission and

Strategy Themes • Strategy Development – Outcomes, Goals

and Objectives • Performance Management – Target Setting

and Action Planning • Critical Success Factors (CSF’s) and Key

Performance indicators (KPI’s) • Business Process Management (BPM) • Balanced Scorecard Method • Planning and Strategy

– (foundation, intermediate & advanced)

• Modelling and Forecasting – (foundation, intermediate & advanced)

• Threat Assessment & Risk Management – (foundation, intermediate & advanced)

• Layers of Power, Trust and Reputation • Leadership Studies, Goal-seeking and

Stakeholder Analysis • Military Science, Peace and Conflict

Studies – War, Terrorism and Insecurity • Corporate Finance and Strategic

Investment Planning Futures • Management Science and Business

Administration Futures • Future Management and Analysis of Global

Exploitation of Natural Resources • Social Networks and Connectivity • Consumerism and the rise of the new

Middle Classes • The BRICs and emerging powers

– • Brazil • Russia • India • China • • The Seven Waves of Globalisation

– • Goods • People • Capital • Services – • Ideology • Economic Control •

– • Geo-Political Domination •


• Human Values, Ethics and Beliefs • History, Culture and Human Identity • Human Geography & Industrial Futures • Human Factors and Behavioural Theory • Anthropology, Sociology and Factors of

Cultural Change • Human Rites, Rituals and Customs - the

Future of Cults, Sects and Tribalism • Ethnographic and Demographic Futures • Epidemiology, Morbidity and Actuarial

Science Futures • Infrastructure Strategy, Regional Master

Planning and Urban Renewal • Future Townscape Envisioning. Planning

Modelling and Virtual Terrain Mapping • The Future of Urban and Infrastructure

Master Planning, Zoning and Control • Architecture and Design Futures - living

in the Built Environment of the Future

• Trans-humanism – The Future Human State – Qualities, Capabilities, Capacities

• The Future of Medical Science, Bio-Technology and Genetic Engineering

• The Future of the Human Condition - Health, Wealth and Wellbeing

• The Future of Biomechanics, Elite Sports and Professional Athletics

• Personal Futures – Motivational Studies, Life Coaching and Personal Training

• Positive Thinking – Self-Awareness, Self-Improvement & Personal Development

• Positive Behavioural Psychology and Cognitive Therapy - NLP and EHT

• Intuitive Assimilation and Cognitive Analysis

• Predictive Envisioning and Foresight Development

• Contemplative Mediation and Psychic Methods


• Business Strategy, Transformation and Programme Management Futures

• Next Generation Enterprises (NGE) – Envisioning, Planning and Modelling

• Multi-tier Collaborative Future Business Target Operating Models (eTOM)

• Corporate Responsibility / Triple Bottom Line Management

• Regulatory Compliance - Enterprise Governance, Reporting and Controls

• Future Economic Modelling, Long-range Forecasting and Financial Analysis

• The Future of Organisational Theory and Operational Analysis

• Business Innovation and Product Planning Futures

• Technology Innovation and Product Design Futures

• Product Engineering and Production Planning Futures

• Enterprise Resource Planning and Production Management Futures

• Marketing Needs Analysis, Propositions and Product Life-cycle Management

• The Future of Marketing Services, Communications and Advertising

• The Future of Media, Entertainment and Multi-channel Communications

• The Future of Leisure, Travel & Tourism – Culture, Restaurants and Entertainment

• The Future of Spectator Events - Elite Team Sports and Professional Athletics

• The Future of Art, Literature and Music • The Future of Performance Arts, Theatre

and the Moving Image • Science Fiction & Images of the Future • Interpreting Folklore, Legends & Myths –

Theology, Numerology & Lexicography • Utopian and Dystopian Literature, Film

and Arts

Strategic Foresight as Knowledge Management

Thinking about the Future…..

• The way that we think about the future must mirror how the future actually unfolds. As we have learned from recent experience, the future is not a straightforward extrapolation of simple, single-domain trends. We now have to consider ways in which the possibility of random, chaotic and radically disruptive events may be factored into enterprise threat assessment and risk management frameworks and incorporated into enterprise decision-making structures and processes.

• Managers and organisations often aim to “stay focused” and maintain a narrow perspective in dealing with key business issues, challenges and targets. A concentration of focus may risk overlooking those Weak Signals indicating potential issues and events, agents and catalysts of change. These Weak Signals – along with their resultant Wild Cards, Black Swan Events and global transformations - are even now taking shape at the very periphery of corporate awareness, perception and vision – or even just beyond.

• These agents of change may precipitate global impact-level events which either threaten the very survival of the organisation - or present novel and unexpected opportunities for expansion and growth. The ability to include weak signals and peripheral vision into the strategy and planning process may therefore be critical in contributing towards the organisation's continued growth, success, well being and survival.


THINKING ABOUT THE FUTURE -

• It has long been recognized that one of the most important competitive factors for any organization to master is the management of uncertainty. Uncertainty is the major intangible factor contributing towards the risk of failure in every process, at every level, in every type of business.

• The way that we think about the future must mirror how the future actually unfolds. As we have all learned from recent experience, the future is not a simple extrapolation of linear, single-domain trends. We now have to consider ways in which the possibility of random, chaotic and radically disruptive events may be factored into enterprise strategy development, threat assessment and risk management frameworks and incorporated into enterprise decision-making structures and processes.


THINKING ABOUT THE FUTURE -



Futures Studies

• Futures Studies, Foresight, or Futurology is the science, practice and art of postulating possible, probable, and preferable futures. Futures studies (colloquially called "Futures" by many of the field's practitioners) seeks to understand what is likely to continue, what is likely to change, and what is a novel, emerging pattern or trend. Part of the discipline thus seeks a systematic and extrapolation-based understanding of both past and present events - in order to determine the probability and impact of future events, patterns and trends.

• Futures is an interdisciplinary curriculum, studying yesterday's and today's changes, and aggregating and analyzing both lay and professional content and strategies, beliefs and opinions, forecasts and predictions with respect to shaping tomorrow. It includes analysing the sources, agents and causes, patterns and trends of both change and stability in an attempt to develop foresight and to map possible, probable and alternative futures.

Foresight

• Foresight draws on traditions of work in long-range forecasting and strategic planning horizontal policymaking and democratic planning, horizon scanning and futures studies (Aguillar-Milan, Ansoff, Feather, van der Hijden, Slaughter et all) - but was also highly influenced by systemic approaches to innovation studies, global design, massive change, science and technology futures, economic, social and demographic policy, fashion and design - and the analysis of "weak signals" and "wild cards", "future trends“ "critical technologies“ and “cultural evolution".

– The longer-term - futures that are usually at least 10 years away (though there are some exceptions to this, especially in its use in private business). Since Foresight is an action-oriented discipline (via the planning link) it will rarely be applied to perspectives beyond a few decades out. Where major infrastructure decisions such as petrology reservoir exploitation, aircraft design, power station construction, transport hubs and town master planning decisions are concerned - then the planning horizon may well be half a century.

– Alternative futures: it is helpful to examine alternative paths of development, not just what is currently believed to be most likely or business as usual. Often Foresight will construct multiple scenarios. These may be an interim step on the way to creating what may be known as positive visions, success scenarios or aspirational futures. Sometimes alternative scenarios will be a major part of the output of a Foresight study, with the decision about what preferred future to build being left to other mechanisms (Planning and Strategy).

Strategic Foresight

• Strategic Foresight is the ability to create and maintain a high-quality, coherent and functional forward view, and to use the insights arising in useful organisational ways. For example to detect adverse conditions, guide policy, shape strategy, and to explore new markets, products and services. It represents a fusion of futures methods with those of strategic management (Slaughter (1999), p.287).

• Strategic Envisioning – Future outcomes, goals and objectives are defined via Strategic Foresight and are determined by design, planning and management - so that the future becomes realistic and achievable. Possible futures may comply with our preferred options - and therefore our vision of an ideal future and desired outcomes could thus be fulfilled.

– Positivism – articulating a single, preferred vision of the future. The future will conform to our preferred options - thus our vision of an ideal future and desired outcomes will be fulfilled.

– Futurism – assessing possible, probable and alternative futures – selecting those futures offering conditions that best fit our strategic goals and objectives for achieving a preferred and desired future. Filtering for a more detailed analysis may be achieved by discounting isolated outliers and focusing upon those closely clustered future descriptions which best support our desired future outcomes, goals and objectives.

Risk Management

• Risk Management is a structured approach to managing uncertainty through foresight and planning. A risk is related to a specific threat (or group of related threats) managed through a sequence of activities using various resources: -

– Risk Research – Risk Identification – Scenario Planning & Impact Analysis – Risk Assessment – Risk Prioritization – Risk Management Strategies – Risk Planning – Risk Mitigation

• Risk Management strategies may include: - – Transferring the risk to another party – Avoiding the risk – Reducing the negative effect of the risk – Accepting part or all of the consequences of a particular risk .

• For any given set of Risk Management Scenarios, a prioritization process ranks those risks with

the greatest potential loss and the greatest probability of occurrence to be handled first – and those risks with a lower probability of occurrence and lower consequential losses are then handled subsequently in descending order of impact.

• In practice this prioritization can be challenging. Comparing and balancing the overall threat of risks with a high probability of occurrence but lower loss -versus risks with higher potential loss but lower probability of occurrence -can often be misleading.

Scenario Planning and Impact Analysis

• Scenario Panning and Impact Analysis: - In any Opportunity / Threat Assessment Scenario, a prioritization process ranks those risks with the greatest potential loss and the greatest probability of occurring to be handled first - subsequent risks with lower probability of occurrence and lower consequential losses are then handled in descending order. As a foresight concept, Wild Card or Black Swan events refer to those events which have a low probability of occurrence - but an inordinately high impact when they do occur.

– Risk Assessment and Horizon Scanning have become key tools in policy making and strategic planning for

many governments and global enterprises. We are now moving into a period of time impacted by unprecedented and accelerating transformation by rapidly evolving catalysts and agents of change in a world of increasingly uncertain, complex and interwoven global events.

– Scenario Planning and Impact Analysis have served us well as a strategic planning tools for the last 15 years or so - but there are also limitations to this technique in this period of unprecedented complexity and change. In support of Scenario Planning and Impact Analysis new approaches have to be explored and integrated into our risk management and strategic planning processes.

• Back-casting and Back-sight: - “Wild Card” or “Black Swan” events are ultra-extreme manifestations with a very low probability of, occurrence - but an inordinately high impact when they do occur. In any post-apocalyptic “Black Swan Event” Scenario Analysis, we can use Causal Layer Analysis (CLA) techniques in order to analyse and review our Risk Management Strategies – with a view to identifying those Weak Signals which may have predicated subsequent appearances of unexpected Wild Card or Black Swan events.

Weak Signals and Wild Cards

• “Wild Card” or "Black Swan" manifestations are extreme and unexpected events which have a very low probability of occurrence, but an inordinately high impact when they do happen Trend-making and Trend-breaking agents or catalysts of change may predicate, influence or cause wild card events which are very hard - or even impossible - to anticipate, forecast or predict.

• In any chaotic, fast-evolving and highly complex global environment, as is currently developing and unfolding across the world today, the possibility of any such "Wild Card” or "Black Swan" events arising may, nevertheless, be suspected - or even expected. "Weak Signals" are subliminal indicators or signs which may be detected amongst the background noise - that in turn point us towards any "Wild Card” or "Black Swan" random, chaotic, disruptive and / or catastrophic events which may be on the horizon, or just beyond......

• Back-casting and Back-sight: - In a post-apocalyptic Black Swan Event Scenario, we can use Causal Layer Analysis (CLA) techniques in order to analyse and review our Risk Management Strategies to identify those Weak Signals which may have predicted, suggested, pointed towards or indicated subsequent Wild Cards or Black Swan Events – in order to discover changes and improvements to strengthen Enterprise Risk Management Frameworks.


• Foresight and Precognition – Contemplative, mystic, meditative and psychic methods for pre-cognitive viewing of the future and how the future will unfold. These activities have been recorded throughout history (Josephus, Nostradamus) and are well known within certain cultures (Central American Indians) and government agencies (US and Soviet Military) - and may also involve the use of hypnotic or hallucinogenic states.

• The Intelligence Revolution – Artificial Intelligence will revolutionise homes, workplaces and lifestyles - and new virtual worlds will become so realistic that they will rival the physical world. Robots with human-level intelligence may finally become a reality, and at the ultimate stage of mastery, we'll even be able to merge human capacities with machine intelligence and attributes – via the man-machine interface.

• The Biotech Revolution – Genetics and biotechnology promise a future of unprecedented health and longevity: DNA screening could prevent many diseases, gene therapy could cure them and, thanks to laboratory-grown organs, the human body could be repaired as easily as a car, with spare parts readily available. Ultimately, the ageing process itself could be slowed or even halted.

• Trans-humanism – advocates the ethical use of technology to expand current human capacities, supporting the use of future science and technology to enhance human capabilities and qualities, in order to overcome undesirable and unnecessary aspects of the present human condition.

• The Quantum Revolution – The quantum revolution could turn many ideas of science fiction into science fact - from meta-materials with mind-boggling properties like invisibility through limitless quantum energy and room temperature superconductors to Arthur C Clarke's space elevator. Some scientists even forecast that in the latter half of the century everybody will have a personal fabricator that re-arranges molecules to produce everything from almost anything. Yet how will we ultimately use our mastery of matter? Like Samson, will we use our strength to bring down the temple? Or, like Solomon, will we have the wisdom to match our technology?


• Renewable Resources. Any natural resource is a renewable resource if it is replenished by natural processes at a rate comprisable to or faster than its rate of consumption by humans or other users. Some renewable resources - solar radiation, tides, wind and hydroelectricity, nuclear fusion - are also classified as perpetual resources, in that they will never be able to be consumed at a rate in excess of their long-term availability or renewal. The term renewable resource also carries the implication of prolonged or perpetual sustainability for the processing and absorption of waste products via natural ecological and environmental processes.

• Sustainability is a characteristic of a process or mechanism that can be maintained indefinitely at a certain constant level or state – without showing any long-term degradation, decline or collapse.. This concept, in its environmental usage, refers to the potential longevity of vital human ecological support systems - such as the ecology, environment the and man-made systems of agriculture, industry, forestry, fisheries - and the planet's climate and natural processes and cycles upon which they depend.

• Global Massive Change is an evaluation of global capacities and limitations. It includes both utopian and dystopian views of the emerging world future state, in which climate, the environment and geology are dominated by human manipulation –

– Human impact is now the major factor in climate change and environmental degradation. – Extinction rate is currently greater than in the Permian-Triassic boundary extinction event – Man now moves more rock and earth than do natural geological processes.

• In the past, many complex human societies (Clovis, Mayan, Easter Island) have failed, died out or just simply

disappeared - often as a result of either climate change or their own growth-associated impacts on ecological and environmental support systems. Thus there is a clear precedent for modern industrial societies - which continue to grow unchecked in terms of globalisation complexity and scale, population growth and drift, urbanisation and environmental impact – societies which are ultimately unsustainable, and so in turn must also be destined for sudden and catastrophic instability, failure and collapse.

Complexity Paradigms

System Complexity is typically characterised by the number of elements in a system, the number of interactions and the nature (type) of those interactions. One of the

problems in addressing complexity issues has always been distinguishing between the large number of elements and relationships, or interactions evident in chaotic

(unconstrained) systems - and the still large, but significantly smaller number of elements and interactions found in ordered (constrained) systems. Orderly

Frameworks act to both reduce the total number of elements and interactions – with fewer and smaller classes of more-uniform elements – and with less regimes of

reduced size featuring more highly-ordered, internally correlated and constrained interactions – as compared with Disorderly Frameworks.


• Simplexity (Reductionist) Paradigm – Linear Systems & Chaotic Interaction – Linear Systems and Game Theory – War-gaming and Lanchester Theory

• Entropic (Ordered, Systemic, Mechanistic) Paradigm – Structural Futurism

– Complex Adaptive Systems (CAS)

• Complexity (Constructionist) Paradigm – Complex Systems & Chaos Theory – Complex Ordered Systems – Complex Disordered Systems

• Uncertainty (Random, Chaotic, Disorderly, Enthalpy) Paradigm – Disruptive Futurism – Cosmology – Climatology – Black Swan Events - Weak Signals, Wild Cards, Chaos, Uncertainty & Disruption

Complexity (Constructionist) Paradigm – Complex Systems and Chaos Theory

• Complexity tends to be used to characterize systems with many elements or parts arranged in a complex or intricate relationship. The study of these complex linkages, relationships, or interactions between elements is the main goal of network theory and network science. In science there are a number of approaches to characterizing complexity, many of which are reflected in this Paradigm. In a business context, complexity management is the methodology to minimize value-destroying complexity and efficiently control value-adding complexity in a cross-functional system approach.

• Definitions are often tied to the concept of a "system"—a set of parts or elements which have relationships among them differentiated from relationships with other elements outside the relational framework or regime. Many definitions tend to assume that complexity expresses a condition with numerous elements in a system and numerous instances and types of relationships between the elements. Simplexity, the sister paradigm to Complexity, helps us to differentiate between the analysis of complex systems and reduction of complex systems into multiple simple systems.

• Warren Weaver has postulated that the complexity of any particular system is the degree of difficulty in predicting system outcomes when the properties of the system's parts and relationships are known and understood. Other definitions relate Complexity to the probability of encountering any given condition in a system once the behaviours or characteristics of the system have been specified. In Weaver's view, complexity comes in two forms: Disorganized Complexity and Organized Complexity.

• From Wikipedia, the free encyclopedia

http://en.wikipedia.org/wiki/Network_theory

http://en.wikipedia.org/wiki/Network_science

http://en.wikipedia.org/wiki/Science

http://en.wikipedia.org/wiki/Complexity_management

http://en.wikipedia.org/wiki/System

http://en.wikipedia.org/wiki/Warren_Weaver

http://en.wikipedia.org/wiki/System


• One of the problems in addressing complexity issues has always been distinguishing conceptually between the large number of elements and relationships, or interactions evident in chaotic (unconstrained) systems - and the still large, but significantly smaller number of elements and interactions found in ordered (constrained) systems. Order acts to both reduce the number of elements and interactions - and at the same time creates smaller regimes of more-uniform, ordered or correlated, interactions.

• Weaver perceived and addressed this problem, in at least a preliminary way, by drawing a distinction between "disorganized complexity" and "organized complexity".

• Weaver's paper has influenced contemporary thinking about complexity. In Weaver's view, Disorganized Complexity results from a system having a very large number of parts - say millions. billions or many more. Though the interactions of these parts in a "disorganized complexity" paradigm can be seen as random – thus properties of the system as a whole can be understood by using probability and statistical analysis. System size, therefore, brings with it a new type of Complexity - all of its own…..

• Organized Complexity, in Weaver's view, resides in the property of a non-random, ordered, or correlated, interaction between the parts. These correlated relationships create a differentiated structure which can act as a system and interact freely with other systems. The coordinated system exhibits properties that are not carried by, or dictated by, its individual parts. The organisational aspect of this form of complexity compared with other types of system is that the subject system “develops”, “emerges! or “evolves” without any external intervention from any form of "guiding hand".

http://en.wikipedia.org/wiki/Warren_Weaver


Complexity theory has been used extensively in the field of Futures Studies, Strategic Management, Organisational Theory and Operational Analysis. It is applied in these domains to understand how organisations or enterprises adapt to their environment. The theory treats organizations and firms as collections of strategies and structures. When organisations or enterprises demonstrate properties of Complex Adaptive Systems (CAS) - which is often defined as consisting of a small number of relatively simple and loosely connected systems - then they are much more likely to adapt to their environment and, thus, survive the impact of change and random events. Complexity theory thinking has been present in strategic and organisational studies since the first inception of Complex Adaptive Systems (CAS) as an academic discipline. Complex Adaptive Systems are contrasted with ordered and chaotic systems by the relationship that exists between the system and the agents and catalysts of change which act upon it. In an ordered system the level of constraint means that all agent behaviour is limited to the rules of the system. In a chaotic system these agents are unconstrained and are capable of random events, uncertainty and disruption. In a CAS, both the system and the agents co-evolve together; the system acting to lightly constrain the agents behaviour - the agents of change, however, modify the system by their interaction. CAS approaches to strategy seek to understand both the nature of system constraints and change agent interactions and generally takes an evolutionary or naturalistic approach to scenario planning and strategy development

Simplexity (Reductionist) Paradigm – Linear Systems and Chaotic Interaction • Simplexity • has it’s origins in the field of Science - Jack Cohen (the scientist) and his

collaborator Ian Stewart are authors of the book “The Collapse of Chaos” (1995), a non-fiction work that attempts to explain chaos theory and complex systems to a general audience.. The complexity of algorithms and of mathematical problems is one of the core subjects of theoretical computer science – which prompted computer scientists Broder and Stolfi to whimsically describe • Simplexity • as a concept worthy of just as much attention as its twin paradigm, complexity, attracts.

• Simplexity • has been popularised in the book “Simplexity: Why Simple Things Become

Complex (and How Complex Things Can Be Made Simple)” by Jeffrey Kluger – which describes some of the ways in which simplexity theory can be applied to many scenarios across multiple disciplines. Kluger offers a look at simplexity in economics, sports, linguistics, technology, medicine, and human behaviour. Simplexity also provides insight into how futurists and strategists can improve their frameworks, paradigms and models - by understanding how the interplay of simplicity (Linear Systems) and chaos (the possibility of random events introducing uncertainty and disruption) can form both complexity (Complex Adaptive Systems) and also simplexity (reduction of Complex Systems into an integrated set of linear or simplistic systems interacting with random events).

• Simplexity • is an intriguing future paradigm that will drive new thinking in many novel,

exciting and surprising directions over the coming years. • Simplexity • is an elegant and pleasing paradigm which will feature prominently for a good while into the future.

Simplexity (Reductionist) Paradigm – Linear Systems and Chaotic Interaction Michelangelo was once asked how he created his sculptures. “I take a stone – and

remove anything which is not required”. – thus demonstrating that a beautiful artefact may be created by the removal of everything which detracts from the intrinsic simplicity of that beauty. • Simplexity • shows itself in subtle design that at first glance appears to be something plain and simple; easy to use as well as beautiful to behold – but on closer inspection it becomes apparent that the artefact is constructed from many harmonious layered components. The complex functionality of the design is muted and disguised as sophisticated components integrated within the • Simplexity • paradigm of a compelling idea, elegant functional concept or simple design vision.

In the future, a consumer-oriented Western world, faced with diminishing availability of natural resource and increased costs - may become less materialistic and consumption-focused as we are driven to think more carefully about how we use and recycle valuable possessions. Smart Devices (Laptops, Tablets and Smart Phones) with intuitive user interfaces are lifestyle accessories and high-status fashion items which may be continuously and easily personalised, customised and configured to uniquely respond to their owners changing needs – thus complying with the simple beauty of the • Simplexity • paradigm.

Simplexity (Reductionist) Paradigm – Linear Systems and Chaotic Interaction Already, trade mechanisms are emerging to recycle these expensive • Simplexity •

items for refurbishment and re-sale – as an integral part of a new and emerging strategy for sustainably acquiring and replacing our artefacts and goods. We already have has systems and processes in place for re-using and re-cycling vehicles for many years. This trend will tend to drive manufacturers to make fewer, but better quality artefacts – and in turn multi-owner consumer behaviour will have to be reflected in the future Recycling processes - Recovery. Refurbishment and Resale - embedded in the corporate planning and strategy of manufacturing economies – such as China.

• Simplexity • artefacts will have a longer useful lifespan under multiple owners in

order to continue to offer a Lifetime Cost of Ownership which remains inexpensive in real terms. This will bring many challenges to manufacturing enterprises – with their responsibility to recover and recycle expensive items for re-manufacturing, refurbishment and re-sale - and at the same time have to contend with a transformation of domestic and export markets in which demand for new goods is cyclical – fragile when new ideas are scarce - strong when multiple trends are emerging and interacting. In order to succeed, businesses will become increasingly transparent, collaborative and interactive with consumers – or face becoming unable to compete effectively. • Simplexity • artefacts will cost more, but still remain relatively inexpensive in real terms in order to pander to the perfidious wishes and desires of western consumers.

Space-Time and Energy-Matter

Randomness The Nature of Uncertainty

The Nature of Uncertainty – Randomness

Classical (Newtonian) Physics – apparent randomness is as a result of Unknown Forces Relativity Theory – any apparent randomness or asymmetry is as a result of Quantum effects

Quantum Mechanics – all events are truly and intrinsically both symmetrical and random Wave (String) Theory –apparent randomness and asymmetry is as a result of Unknown Forces

Minkowski Space-time Continuum

• In1907 the German mathematical physicist Hermann Minkowski developed the concept

of a single space-time continuum - which provides a conceptual framework for all the

mathematical proofs used in relativity - including Albert Einstein's general and special

theory of relativity. Minkowski space-time is an integrated and unified four-dimensional

continuum - composed of three Positional Dimensions (Loci or Vectors x, y and z

coordinates) defining Space (vector / position) – which is entirely integrated and wholly

unified with a fourth Temporal Dimension (t coordinate) – defining Time (history).

• Minkowski quickly realised that the preliminary work on relativity theory could best be

explained and understood in a multi-dimensional universe which extended beyond the

three spatial dimensions (x, y and z axes) - to include a temporal dimension (t axis) - as

the foundation of a new, non-Euclidean four-dimensional geometry. Minkowski coupled

the two separate dimensions of Space and Time together to create a unified four-

dimensional Space-Time continuum - which was then employed in his own treatment of

a four-dimensional study of electrodynamics. This study involved a combination of two

previously separate systems – Space (with x, y and z axes) and Time (t axis) – to form

Space-Time (with x, y, z and t axes). He noticed that the invariant interval between

two events shared some of the properties of distance in Euclidean three-dimensional

geometry and formulated this invariant interval as the square root of a sum and

difference of squares of the intervals of both Space and Time.

Minkowski

Space-Time continuum

• In an attempt to understand the previous works of Lorentz and Einstein, during 1907 Hermann Minkowski synthesised a revolutionary four-dimensional view of a single, integrated space-time continuum.

• Until the development of Minkowski space-time continuum - the three-dimensional coordinate system describing Space (position) in Classical (Newtonian) physics and the other universal dimension, the flow of Time, were considered to exist independently.


• Space (position) and Time (history) flow inextricably together in a single direction –

towards the future – just as a river can only flow downhill, towards the sea. Space and

Time can only exist together within a single, unified Space-Time continuum. Without

Space – there can be no Time , and without Time – there can be no Space.

• Minkowski space-time is also often referred to as Minkowski space or the Minkowski

universe. . In order to exploit the principles of the Minkowski space-time continuum, this

type of coupling must fully demonstrate that the history of a particle or the

transformation of a process over time is dependent on both its spatial and historical

components. Minkowski space-time is used predominately in the study of relativity,

although it can also be applied to other subjects and fields of human endeavour

involving the coupling of time and spatial vectors –for example, in “Big Data” which is

used for Predictive Analytics, Geospatial Propensity Modelling and Future Analysis. .


• The three-dimensional coordinate

system describing Space (position) in

Classical (Newtonian) physics along with

the other universal dimension, the flow of

Time (history), were considered to exist

and act entirely independently of each

other - until the synthesis of Space-Time

• During 1907, in an attempt to gain an

understanding of the previous work of

Lorentz and Einstein, the German

Mathematician Hermann Minkowski

developed a four-dimensional view of the

universe as a single, integrated and

unified Space-Time continuum.

• In order to demonstrate the principle

properties of the Minkowski Space-

Time continuum – any type of Spatial

and Temporal coupling must be able to

show over time that the History of a

particle or the Transformation of a

process is fully and entirely dependent

on both its Spatial (positional) and

Temporal (historic) components.


• Using this concept, events which are localized in both space and time may be

considered as the analogues of points in three-dimensional geometry. Thus the Time

dimension in the history of a single particle or the timeline of an event in Minkowski

space-time - resembles the arc of a curve in a three-dimensional Space, and is thus

fully dependent on both its spatial and historical components.

• Like Space, Time is a Dimension – but Time only flows in a single direction, as does a

River. Time and Space can only exist together within a single, unified Space-Time

continuum. Without Time – there can be no Space, and without Space – there can be

no Time. Minkowski space-time is also often referred to as Minkowski space or the

Minkowski universe. Minkowski space-time is used predominately in the study of

relativity, although it can also be applied to other subjects involving the coupling of

spatial and temporal vectors – such as Futures Studies. In order to exploit the

Minkowski space-time continuum, this type of coupling must demonstrate that the

history of a particle or the transformation of a process over time is fully dependent

on both Space and Time.

Minkowski

Space-Time continuum

• Space (position) and

Time (history) flow

inextricably together in

a single direction –

towards the future.

• In order to exploit the

principle properties of

the Minkowski space-

time continuum, any

type of Spatial and

Temporal coupling

must be able to fully

demonstrate that the

History of a particle

or the Transformation

of a process over time

is entirely dependent

on both its spatial and

historical components.

Heat Death of the Universe

• Space (position) and Time (history) flow inextricably together in a single direction –

towards the future – just as a river can only flow downhill, towards the sea. Space

and Time can only exist together within a single, unified Space-Time continuum.

• Along the path leading towards Universal Heat Death is the theory of increasing

Universal entropy (disorder, chaos). Over time, the universe is experiencing a slow

dissipation of its stored mechanical energy. The second law of thermodynamics states

that entropy tends to increase within an isolated thermal system. The concept at the

heart of Heat Death is that the universe is the largest isolated system in existence, and

it naturally has an entropy which increases over time. As time goes on, the entropy

(chaos) in the universe will approach its potential maximum – at which point Relativity,

Chemical, Physical, Thermodynamic and Quantum universal processes would cease.

• There is some speculation that entropy may be stored in black holes. As black holes

evaporate radiation, they consequently release stored entropy (which is what causes

objects to get sucked into them) - which in turn increases universal entropy. In order

for the universe to exist, the entropy level has to remain below the maximum value, as

it does now. It is estimated that we are currently at an entropy level of 10^104, while

the maximum remains at 10^122. We are a long way from reaching this level, but

entropy today is significantly higher than that of the very early universe (10^88).

http://en.wikipedia.org/wiki/Second_law_of_thermodynamics

Heat Death of the Universe

• As the universe eventually reaches total thermal equilibrium, anything which remains in

the Universe will be at absolute zero. Atoms and subatomic particles inside of the nuclei

of stars will begin a slow decay and evaporation - by releasing radiation (the epoch of

degeneration). As matter continues to be released as radiation, over an unimaginable

long period of time even the ultra-massive black holes at the centre of Galaxies will

ultimately evaporate away to nothing. Eventually there will no longer be any physical

matter left within the universe. Finally, the night sky will become both completely empty

and totally dark – the dark era. When the last atom eventually decays, evaporates and

disappears, there is no longer anything left to mark the passage of time. Without Matter

and Energy, there can be no Space. Without Space, there can be no Time, and without

Time, there can be no Universe. So Space collapses and disappears – the “Big Crunch”.

• The universe is so vast, and is still constantly expanding – so there is still much energy in

the universe and still much variation in the amount of energy between different places (a

multi-thousand degree Kelvin star vs. empty space at just above absolute zero), so that

Heat Death of the Universe is not going to happen for 10^37 years – by which time all

matter will have decayed, evaporated and radiated away – so all that remains is radiation.

As discussed before, without Space, there can be no Time, and without Time, there can

be no Space – resulting in the end (“heat-death”) of the Universe – the “Big Crunch”.

The Flow of Information through Time

• Time Present is always in some way inextricably woven into both Time Past and Time

Future – with the potential, therefore, to give us notice of future random events – before

they actually occur. Chaos Theory suggests that even the most subliminal inputs, so

minute as to be undetectable, may ultimately be amplified over many system cycles – to

grow in influence and effect to trigger dramatic changes in future outcomes. So any

given item of Information or Data (Global Content) may contain faint traces which hold

hints or clues about the outcomes of linked Clusters of Past, Present and Future

Events.

• Every item of Global Content that we find in the Present is somehow connected with

both the Past and the Future. Space-Time is a Dimension – which flows in a single

direction, as does a River. Space-Time, like water diverted along an alternative river

channel, does not flow uniformly – outside of the main channel there could well be

“submerged objects” (random events) that disturb the passage of time, and may

possess the potential capability of creating unforeseen eddies, whirlpools and currents

in the flow of Time (disorder and uncertainty) – which in turn posses the capacity to

generate ripples, and waves (chaos and disruption) – thus changing the course of the

Time-Space continuum. “Weak Signals” are “Ghosts in the Machine” of these

subliminal temporal interactions – with the capability to contain information about future

“Wild card” or “Black Swan” random events.

Space-time Disturbances

• Time, like Water, does not flow uniformly – outside the depths of the main

channel within which Time travels, there may also be submerged objects

(random events) that posses the ability to cause disturbances, eddies and

currents in the flow (disorder and uncertainty) – which in turn have the capacity

to generate ripples, whirlpools and waves (chaos and disruption) that flow

through the Space-Time continuum bringing with it the possibility for change -

thus precipitating novel and unexpected outcomes.

• These unpredictable temporal interactions (random events) may interact with

current and emerging waves, patterns and trends to cause Chaos, Disorder,

Uncertainty and Disruption – which in turn have the capacity tp generate Wild

Card or Black Swan Events – manifestations of randomness that act in such a

way as to prevent Time flowing smoothly and uniformly towards an unerringly

predictable outcome or conclusion. Random Events change the flow of Time –

thus the deflected course taken by Time interacting with Random Events means

that the Future becomes unpredictable. Instead of smooth, linear outcomes – we

experience surprises.

Geological Timelines



introduces into our daily lives. Research into stochastic (random) processes

looks towards how we might anticipate, prepare for and manage the chaos and

uncertainty which acts on complex systems – including natural systems such as

Cosmology and Climate, as well as human systems such as Politics and the

Economy - in order that we may anticipate future change and prepare for it…..


– Any apparent randomness is as a result of Unknown Forces


– Apparent randomness or asymmetry is as a result of Quantum effects


– Every Quantum event is truly and intrinsically both symmetrical and random

• Wave Mechanics (String Theory)

– Any apparent randomness and asymmetry is as a result of Unknown Forces

Space-Time v. Energy-Matter Domain Object Process Outcome Timeline Size Range

Classical Mechanics

(Newtonian Physics)

Common, Everyday

and Celestial Objects

Motion Change of

Position

4.6 x 10.(12) yr 1 Solar Mass

Thermodynamics Energy (Entropy and

Enthalpy)

Transformation

and Flow

Change in State 10.(37) yr 10.(34) atoms

Biology Organisms Evolution Life and Death 3.7 x 10.(12) yr

Chemistry Molecules Transformation Change in State 1.37 x 10.(13) yr

Atomic Theory Atoms Interaction Change in State 1.37 x (10) 13 yr

Quantum Mechanics Sub-atomic particles Interaction Objects created

and destroyed

10.(37) yr 10.(34) atoms

Geology Earth Transformation Change in State

and Position

4.6 x 10.(12) yr 1 Earth Mass

Astronomy Observable Celestial

Objects

Motion Change in State

and Position

1.37 x 10.(13) yr 10.(24) solar

masses


Celestial Objects

Transformation Change in State

and Position

10.(37) yr 10.(34) atoms

Relativity Theory The Universe Interaction Change in State

and Position

10.(37) yr 10.(34) atoms

Wave Mechanics

(String Theory or

Quantum Dynamics)

The Universe,

Membranes and

Hyperspace

Motion, Flow,

Transformation

and Interaction

Objects created

and destroyed,

with change in

State and Position

10.(37) yr 10.(34) atoms

Space-Time v. Energy-Matter






– governs the behaviour of unimaginably small objects (fundamental sub-atomic particles)











Wave Mechanics (String Theory) Metaphysical Philosophy


metaphysical

philosophy

In Western

philosophy,

metaphysics has

become the study of

the fundamental

nature of reality -

what it is, why it is,

and how we are to

understand its

properties.....

Related: -

Alchemy

Ontology

Taxonomy

Classification

Natural History

Natural Philosophy

Systemic

Methodology

String Theory is a set of mathematical models which

attempts to address the four known fundamental

interactions – gravitation, electromagnetism, strong

nuclear force, weak nuclear interaction – integrated

into a single, universal Theory of Wave Dynamics. .

In theoretical physics, M-theory is an extension of

string theory in which the 11 dimensions of space-

time are identified as 7 higher-dimensions plus the 4

common dimensions (11D st = 7 hd + 4D).

Proponents believe that the 11-dimensional theory

unites all five 10-dimensional string theories (10D st =

6 hd + 4D) and supersedes them. Though a full

description of the theory is not known, the low-entropy

dynamics are thought to be supergravity interacting

with 2- and 5-dimensional membranes in a single,

unified Theory of Wave Dynamics.

Gabriele Veneziano – is an Italian

theoretical physicist and string

theorist. His 1968 dual resonance

model of the strong interaction was

the first component of string theory to

be described - he is regarded as a

founder of this field of String Theory.

Brian Greene, author of the book

about string theory, The Elegant

Universe, was educated at Harvard

and Oxford, graduating in 1987. After

working at Harvard and Cornell, he is

currently a Professor of Physics and

Mathematics at Columbia.

http://en.wikipedia.org/wiki/Theoretical_physics

http://en.wikipedia.org/wiki/String_theory

http://en.wikipedia.org/wiki/Dimensions




http://en.wikipedia.org/wiki/Supergravity#11d:_the_maximal_SUGRA

http://en.wikipedia.org/wiki/Membrane_(M-Theory)

Wave Mechanics (String Theory)

The Theory of Hyperspace - Prof. Michiu Kaku

• According to this theory, before the Big Bang, our cosmos was actually a perfect ten-dimensional universe, a world where inter-dimensional travel was possible. However, this ten-dimensional universe "cracked" in two, creating two separate universes: a four- and a six- dimensional universe. The universe in which we live was born in that cosmic cataclysm. Our four-dimensional universe expanded explosively, while our twin six-dimensional universe contracted violently, until it shrank to almost infinitesimal size.

• This would explain the origin of the Big Bang. If correct, this theory demonstrates that the rapid expansion of the universe was just a rather minor aftershock of a much greater cataclysmic event, the cracking of space and time itself. The energy that drives the observed expansion of the universe is then found in the collapse of ten-dimensional space and time. According to this theory, the distant stars and galaxies are receding from us at astronomical speeds because of the original collapse of ten-dimensional space and time. This theory predicts that our universe still has a dwarf twin, a companion universe containing the residual dimensions, curled up into a small six-dimensional ball that is too small to be detected or observed.....


String Theory of Hyperspace - Prof. Michiu Kaku

• Many scientists now believe, although we cannot yet prove it, that the multiversity (multiple

universes) hyperspace which contains our own universe – can exist in up to eleven dimensions.

Think of this hyperspace as a multi-dimensional arena in which there are floating a vast number

of bubbles. The surface membrane of every one of these bubbles represents an entire universe,

so our own universe exists on a single bubble membrane. It’s a three dimensional bubble. This

three dimensional bubble is rapidly expanding – according to the Big Bang theory - sometimes

these bubbles can bump into each other, at other times they could split apart – this is the event

that theoretical cosmologists think caused the Big Bang. So we even have a theory of the origin

of the Big Bang itself. In string theory we can have bubbles consisting of different dimensions.

• The highest stable number of dimension in a universe is 11. Universes containing dimensions

beyond 11 become unstable and collapse. When we attempt to describe the mathematics

behind the theory of a 13-, 15-dimensional universe, those universes are intrinsically unstable

and all of them rapidly collapse down to an 11-dimensional universe. Even in the case of an 11-

dimensionsional universe - bubbles can split apart to become 3-dimensional, 4-dimensional , 5-

dimensional and 6-dimensional universes. These bubbles are membranes, so for short we call

them “branes”. Branes may exist with different numbers of dimensions. If we use P to represent

the total number of dimensions belonging to each bubble or membrane – then they become p-

branes. So a p-brane is simply a universe with variable numbers of dimensions – large numbers

of which are floating in a much larger arena - the hyperspace which we discussed earlier.


• String Theory is a set of mathematical models which attempts to address the four known

fundamental interactions – gravitation, electromagnetism, strong nuclear force, weak nuclear

interaction – integrated into a single, universal Theory of Wave Dynamics.

• From the late 1970s, quantum field theory and Einstein's general theory of relativity (classical

theory of gravity) proved to be suitable theoretical frameworks to explain many or most of

observed features of our universe, from elementary particles like electrons and protons to

evolution of the universe in the cosmological scale. There were still , however, many

fundamental problems which remained unexplained and unresolved. The elevation of gravity

to the quantum level remained one of the grand unsolved problems since the days of Einstein,

while other smaller but equally mysterious problems - such as how to solve quantum chromo-

dynamics (QCD), why the cosmological constant of our universe is so small (thought to vanish

at some point but later proven otherwise by observation), and whether properties of black

holes are consistent with quantum principle – still remained unexplained and unresolved.

• Now, 30 years since on, many theoretical physicists seem to believe that string theory did or

will offer answers to many such questions. The original idea of string theory that everything in

nature originates from loops or segments of strings moving in the relativistic way, seemed

ludicrous at first. Yet, its unique ability to define a quantum mechanically consistent gravity is

not something that theorists could easily resist. Existence of gravity in string theory was

recognized as early as 1975, which was then elevated to a realistic computational framework

in 1980's, but putting this to actual use was another problem.


• Better understanding and use of string theory became possible through the realization

in the 1990's that there are hidden symmetries, known as "duality." Recently, it has

been shown that a strongly coupled regime of one superstring theory can sometimes be

understood as a weakly coupled regime of another, "dual" superstring theory. Such

relations demonstrated that different models of superstrings are actually different

perturbative realizations of one and the same theory. One ultimate theory, which was

conjectured to contain all superstring theory as special cases, has been named M

theory. Another lesson from these developments in the 1990's is that string theory is not

only made up of open and closed strings, but all kinds of other extended objects which

are postulated to exist in Hyperspace – including D-branes and M-branes.

• Probably the most celebrated example of dualities, found in 1997 and has been

exploited and generalized widely since then, is AdS/CFT. In its most general

reincarnation, this model asserts equivalence between certain pairs of open string

theory and closed string theory. In practice, one actually considers the limiting cases

where the open string side reduces to a strongly coupled gauge field theory and/or the

closed string side reduces to classical gravitational theory.


• The equivalence offers completely new methods for solving many strongly interacting

theories, most notably quantum chromo-dynamics (QCD). The very acute issue of black

hole in quantum gravity was also addressed through these developments, resulting in a

consensus among many theoretical physicists that quantum principle is probably not

destroyed by existence of quantum black holes in string theory. A complete resolution of

the problem, applicable to all type of black holes is, however, still unavailable.

• Pioneers of string theory such as Michael Green and Michio Kaku hoped that they might

be able to "derive" a unique theory of universe where every fundamental law of nature can

be predicted unambiguously and accurately. With better understanding over the last twenty

years, we now begin to realize that this hope was probably mislaid. String theory is far

more than a single unified theory of the universe. It proved to be a new physical modelling

paradigm and framework – even more so than the ubiquitous quantum field theory.

• Whether and how we can describe a new model of the universe within this framework is a

very highly constrained and difficult problem, which still carries significant uncertainty when

compared to conventional model building methods in particle physics and in cosmology.

Space-Time Analytics – The Temporal Wave

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration of

Time-series Geospatial Data Sets – data with dimensions which exist simultaneously with a Time

(history) and Space (geographic) context. The problems encountered in exploring and analysing

vast volumes of spatial–temporal information in today's data-rich landscape – are becoming

increasingly difficult to manage effectively. In order to overcome the problem of data volume and

scale in a Time (history) and Space (location) context requires not only traditional location–

space and attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with

the additional dimension of time–space analysis. The Temporal Wave supports a new method of

Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.


(timeline) dataset - along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-visual “Big

Data” analytics. The temporal wave combines the strengths of both linear timeline and cyclical

wave-form analysis – and is able to represent data both within a Time (history) and Space

(geographic) context simultaneously – and even at different levels of granularity. Linear and

cyclic trends in space-time data may be represented in combination with other graphic

representations typical for location–space and attribute–space data-types. The Temporal Wave

can be used in roles as a time–space data reference system, as a time–space continuum

representation tool, and as time–space interaction tool.

The Flow of Information through Time

• Time Present is always in some way inextricably woven into both Time Past and Time Future –

with the potential, therefore, to give us notice of future random events – subliminal indications

before they actually occur. Chaos Theory suggests that even the most subliminal inputs, so

minute as to be undetectable, may ultimately be amplified over many system cycles – to grow

in influence and effect to trigger dramatic changes in future outcomes. So any given item of

Information or Data (Global Content) may contain faint traces which hold hints or clues about

the outcomes of linked Clusters of Past, Present and Future Events.

• Every item of Global Content that we find in the Present is somehow connected with both the

Past and the Future. Space-Time is a Dimension – which flows in a single direction, as does a

River. Space-Time, like water diverted along an alternative river channel, does not flow

uniformly – outside of the main channel there could well be “submerged objects” (random

events) that disturb the passage of time, and may possess the potential capability of creating

unforeseen eddies, whirlpools and currents in the flow of Time (disorder and uncertainty) –

which in turn posses the capacity to generate ripples, and waves (chaos and disruption) – thus

changing the course of the Space-Time continuum. “Weak Signals” are “Ghosts in the

Machine” of these subliminal temporal interactions – with the capability to contain information

about future “Wild card” or “Black Swan” random events.

Temporal Disturbances in the Space–Time Continuum

• Weak Signals, Strong Signals, Wild Cards and Black Swan Events – are a sequence of waves linked and integrated in ascending order of magnitude, which have a common source or origin - either a single Random Event instance or arising from a linked series of chaotic and disruptive Random Events - an Event Storm. These Random Events propagate through the space-time continuum as a related and integrated series of waves with an ascending order of magnitude and impact – the first wave to arrive is the fastest travelling,- Weak Signals - something like a faint echo of a Random Event which may in turn be followed in turn by a ripple (Strong Signals) then possibly by a wave (Wild Card) - which may indicate the unfolding a further increase in magnitude and intensity which finally arrives catastrophically - something like a tsunami (Black Swan Event).

Sequence of Events - Emerging Waves Stage View of Wave Series Development

1. Random Event 1. Discovery

2. Weak Signals 1.1 Establishment

3. Strong Signals 1.2 Development

4. Wild Cards 2. Growth

5. Black Swan Event 3. Plateau

4. Decline

5. Collapse

5.1 Renewal

5.2 Replacement

Space-Time Analytics • 4D Geospatial Analytics is the

profiling and analysis of large

aggregated datasets in order to

determine a ‘natural’ structure of

groupings provides an important

technique for many statistical and

analytic applications.

• Environmental and Demographic

Geospatial Cluster Analysis - on the

basis of profile similarities or

geographic distribution - is a statistical

method whereby no prior assumptions

are made concerning the number of

groups or group hierarchies and

internal structure. Geo-spatial and

geodemographic techniques are

frequently used in order to profile and

segment populations by ‘natural’

groupings - such as common

behavioural traits, Clinical Trial,

Morbidity or Actuarial outcomes - along

with many other shared characteristics

and common factors.....

Space-Time Analytics – London Timeline

Space-Time Analytics – London Timeline

• How did London evolve from its creation as a Roman city in 43AD into the crowded, chaotic

cosmopolitan megacity we see today? What will London look like in the future? The London

Evolution Animation takes a holistic view of what has been constructed in the capital over

different historical periods – what has been lost, what is saved and what will be protected.

• Greater London covers 600 square miles. Up until the 17th century, however, the capital city

was crammed largely into a single square mile which today is marked by the skyscrapers which

are a feature of the financial district of the City. Unlike other historical cities such as Athens or

Rome, with an obvious patchwork of districts from different periods, London's individual

structures scheduled sites and listed buildings are in many cases constructed gradually by parts

assembled during different periods. Researchers who have tried previously to locate and

document archaeological structures and research historic references will know that these

features, when plotted, appear scrambled up like pieces of different jigsaw puzzles – all

scattered across the contemporary London cityscape.

• This visualisation, originally created for the Almost Lost exhibition by the Bartlett Centre for

Advanced Spatial Analysis (CASA), explores the historic evolution of the city by plotting a

timeline of the development of the road network - along with documented buildings and other

features – through 4D geospatial analysis of a vast number of diverse geographic,

archaeological and historic data sets.

Spatial versus Temporal Domains Spatial Analysis

(Location)

Temporal Analysis (History)

Sub-atomic

Phenomena Transitive Phenomena

Long-lived Phenomena

Space-Time Continuum

Global Phenomena Economic Analysis

Cosmic Space-Time

Temporal Analysis

Earth Sciences

“Goal-seeking” Empirical Research Domains Applied (Experimental) Science


Applied mathematics

Chemistry

Engineering

Geography

Geology


Archaeology

Palaeontology



Futures Studies

Weather Forecasting

Strategic Foresight


Predictive Analytics


Climate Change

Statistics

Cluster Theory Particle Physics

Quantum Mechanics



Phenomenology

Anthropology and Pre-history

Social Sciences

Sociology




Economics

Life Sciences





Biology Ecology


String Theory

Cosmology

Astronomy

Relativity

Astrophysics

Astrology

Future Management

Pure mathematics




Universal Phenomena

Local Phenomena

Regional Phenomena

Short-lived Phenomena

Atomic Space-Time

Micro-

Phenomena

Randomness

Randomness Patterns in the Chaos




Event Clusters and Connectivity

A

B

C

D

E

G

H

F

The above is an illustration of Event relationships - how Events might be connected. Any detailed,

intimate understanding of the connection between Events may help us to answer questions such as: -

• If Event A occurs does it make Event B or H more or less likely to occur ?

• If Event B occurs what effect does it have on Events C,D,E, F and G ?

Answering questions such as these allows us to plan our Event Management approach and Risk

mitigation strategy – and to decide how better to focus our Incident / Event resources and effort…..


• Aggregated Event includes coincident, related, connected and interconnected Event: -

• Coincident - two or more Events appear simultaneously in the same domain –

but they arise from different triggers (unrelated causal events)

• Related - two more Events materialise in the same domain sharing common

Event features or characteristics (may share a possible hidden common trigger or

cause – and so are candidates for further analysis and investigation)

• Connected - two more Events materialise in the same domain due to the same

trigger (common cause)

• Interconnected - two more Events materialise together in a Event cluster, series

or “storm” - the previous (prior) Event event triggering the subsequent (next) event

in an Event Series…..

• A series of Aggregated Events may result in a significant cumulative impact - and are

therefore frequently identified incorrectly as Wild-card or Black Swan Events - rather

than just simply as event clusters or event “storms”.....


1

2

3

4

5

7

8

6

The above is an illustration of Event relationships - how Risk Events might be connected. A detailed and

intimate understanding of Event clusters and the connection between Events may help us to understand: -

• What is the relationship between Events 1 and 8, and what impact do they have on Events 2 - 7 ?

• Events 2 - 5 and Events 6 and 7 occur in clusters – what are the factors influencing these clusters ?

Answering questions such as these allows us to plan our Risk Event management approach and mitigation

strategy – and to decide how to better focus our resources and effort on Risk Events and fraud management.

Claimant 1

Risk Event

Claimant 2 Residence

Vehicle

Event

Cluster

Aggregated Event Types

A Trigger A

Coincident Events

B Trigger B

Event

Event

C Trigger 1

Related Events

D Trigger 2

Event

Event

E

Trigger

Connected Events

Event

Event F

G Trigger

Inter-connected Events

Event Event

H

Event Complexity Map

Randomness

• Disruptive Future paradigms in Future Studies along with Wave (String) Theory in Physics - alert

us to the possibility of chaotic and radically disruptive Random Events that may generate ripples

which propagate outwards from the causal event like a wave surging across Space-Time.

Some waves might travel through the Space-Time continuum at slightly different speeds due to

the granularity of the substance of the Space-Time Continuum at the most atomic level – or due

to the presence and influence of Unknown Forces (dark energy, dark flow and dark matter).

• Certain types of wave may thus be able to travel faster than others – either due to Unknown

Forces (dark energy, flow, matter), or some types of Wave that propagate through Space-Time

more rapidly than other wave types - or because certain types of wave form can take advantage

of a “short cut” across a “warp” in the Space-Time continuum - a “warp” which brings two discrete

points from different Hyperspace Planes close enough together to allow a Hyperspace Jump.

Complexity Simplicity

Simplexity Ordered

Complexity

Disordered

Complexity Complex Adaptive

Systems (CAS)

Linear

Systems

(element and interaction density)

Chaos Order

Randomness

• Space (position) and Time (history)

flow inextricably together in a single

direction – towards the future. In

order to demonstrate the principle

properties of the Minkowski Space-

Time Continuum, any type of

Spatial and Temporal coupling in a

Model or System must be able to

demonstrate over time that the

History of a particle or the

Transformation of a process are

fully and totally dependent on both its

Spatial (positional) and Temporal

(historic) components - acting

together and in unison.

• Over any given time interval -

multiple Hyperspace Planes stack up

on top of each other to create a time-

line which extends along the

temporal axis of the Minkowski

Space-Time Continuum.

Minkowski Space-Time Continuum.

Randomness

• Neither data-driven nor model-driven representations of the future are able alone, to deal with

the concept of randomness (uncertainty). We therefore need to consider and factor in further

novel and disruptive systemic modelling approaches in order to help us to understand how

both Natural Systems (Cosmology, Climate) and Human Activity Systems (Economics, Crowd

Behaviour) perform.

• Systems Modelling techniques offer us the possibility to manage uncertainty by searching for,

detecting and identifying Weak Signals – which are like the faint seismic disturbances which

warn us of the coming of Earth-quakes and Tsunamis and are always followed by Strong

Signals, Wild Card or Black Swan events – may help us to predict Natural Events like Earth-

quakes and Tsunamis – as well as Human Processes such as the rise and fall of Commodity,

Stocks and Share prices in Global Markets. Weak Signal, Wild Card or Black Swan events

may in this way be factored into our Future Systems Models.


Simplexity Ordered

Complexity

Disordered


Systems (CAS)

Linear

Systems


Chaos Order

Stochastic Processes –

Random Events


Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects – any apparent randomness is as a result of Unknown Forces

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles – all events are truly and intrinsically both symmetrical and random

Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures – any apparent randomness or asymmetry is as a result of Quantum Dynamics

Wave Mechanics (String Theory) – integrates the behaviour of every size & type of object – apparent randomness and asymmetry is as a result of Quantum and Unknown Forces


• Randomness makes precise prediction of future outcomes impossible. We are unable to predict any

outcome with any significant degree of confidence or accuracy – due to the inherent presence of

randomness and uncertainty associated with Complex Systems. Randomness in Complex Systems

introduces chaos and disorder – causing disruption. Events no longer continue along a predictable linear

course leading towards an inevitable outcome – instead, we experience surprises.

• What we can do, however, is to identify the degree of uncertainty present in those Systems, based on

known, objective measures of System Order and Complexity - the number and nature of elements

present in the system, and the number and nature of relationships which exist between those System

elements. This in turn enables us to describe the risk associated with possible, probable and alternative

Scenarios, and thus equips us to be able to forecast risk and the probability of each of those future

Scenarios materialising.

• If true randomness exists and future outcomes cannot be predicted – then what is the origin of

that randomness? For example, are unexpected outcomes simply apparent as a result of sub-

atomic nano-randomness existing at the quantum level – such as uncertainty phenomena etc…..?

• The Stephen Hawking Paradox postulates that uncertainty dominates complex and chaotic systems to

such an extent that future outcomes are both unknown - and unknowable. The working context of this

paradox is restricted, however, to the realm of Quantum Mechanics – where each and every natural event

that occurs at the sub-atomic level is truly intrinsically and completely both symmetrical and random.


• What is the explanation for randomness evident in all high-order phenomena

found in nature…?

• In order to obtain realistic glimpses into the Future, - then the major paradigm

differences between the Actual Reality that we experience every day and our

limited Systemic Models which attempt to simplify, abstract and simulate reality -

must be clearly distinguished between and understood.

• When we design our Systemic Models representing Actual Reality – such as the

Economy, Geo-political systems, Climate Change, Weather and so on - if we are

lucky enough, then some high-order phenomena found in nature may be captured

by a random rule; and with even more luck, by a deterministic rule (which can be

regarded as a special case of randomness) - but if we are unlucky - then those

rules might not be no captured at all. Regarding the nature of reality - it still

remains unclear what factors distinguish truly random phenomenon found in

nature at the Quantum level (e.g. radioactive decay?) from Random Events which

are triggered by unseen forces.


• Can we accept that these natural phenomena are not truly random at all – that is, given sufficient

information such as complete event data sets - it is possible to predict random events? If so,

are all random events the result of the same natural phenomenon - unseen or hidden forces ? “

• Classical (Newtonian) Physics describe the laws which govern all of the systems and objects that we are

familiar with in our everyday routine lives. Relativity Theory, on the other hand, describes unimaginably

large things, whilst Quantum Mechanics describes impossibly small things – and Wave Theory (String

Theory) attempts to describe everything. True randomness does not really exist in Classical (Newtonian)

Physics – the laws which control Chaos and Complex Systems that govern every aspect of our life on Earth

today – from Natural Systems such as Cosmology, Astronomy, Climatology, Geology and Biology through to

Human Activity Systems such as Political, Economic and Sociological Complex Adaptive Systems (CAS).

Randomness is simply the results of those forces which are not known, not recognised, not understood, are

not under the control of the observer or simply occur outside of the known boundaries of observable system

components – but, nevertheless, must still exist and exert influence over the system. Over many System

Cycles, immeasurably small inputs interacting with Complex System components and relationships - may

be amplified into extremely significant outputs.....

1. Classical (Newtonian) Physics – apparent randomness is as a result of Unknown Forces

2. Relativity Theory – any apparent randomness or asymmetry is as a result of Quantum effects

3. Quantum Mechanics – all events are truly and intrinsically both symmetrical and random

4. Wave (String) Theory – apparent randomness and asymmetry is as a result of Unknown Forces -

which may in turn have their origination in Quantum Mechanics effects


Weak Signals, Wild Cards and Black Swan Events

• Economic systems tend to demonstrate Complex Adaptive System (CAS) behaviour – rather than a simple series of chaotic “Random Events” – very similar to the behaviour of living organisms. The remarkable long-term stability and resilience of market economies is demonstrated by the impact and subsequent recovery from Wild Card and Black Swan Events. Surprising pattern changes occur during wars, arm races, and during Republican administrations, causing unexpected stock market crashes - such as oil price shocks and credit crises. Wave-form Analytics for non-stationary time series analysis opens up a new and remarkable opportunity for business cycle studies and economic policy diagnostics.

• The role of time scale and preferred reference from economic observation is explored in detail. For example - fundamental constraints for Friedman's rational arbitrageurs are re examined from the view of information ambiguity and dynamic instability. Alongside Joseph Schumpter’s Economic Wave Series and Strauss and Howe’s Generation Waves, we also discuss Robert Bronson's SMECT Forecasting Model - which integrates both Business and multiple Stock-Market Cycles into its structure.....

• Composite Economic Wave Series

– Saeculum - Century Waves

– Generation Waves (Strauss and Howe)

– Joseph Schumpter’s Economic Wave Series

– Robert Bronson’s SMECT Forecasting Model


• Randomness may be somewhat difficult to demonstrate, as Randomness in chaotic system behaviour is not always readily or easily distinguishable from any other “noise” that we may find in Complex Systems – such as foreground and background wave harmonics, resonance and interference patterns. Complex Systems may be influenced by both internal and external factors which remain hidden - unrecognised or unknown. These unknown and hidden factors may lie far beyond our ability to detect them. The existence of weak internal or external forces simply may not be visible to the observer – the subliminal temporal forces which nevertheless can influence Complex System behaviour in such a way that the presence of imperceptibly tiny inputs, propagated and amplified over many system cycles - are able to create massive observable changes to outcomes in complex system behaviourr.

• Randomness. Neither data-driven nor model-driven macro-economic or micro-economic models currently available to us today - seem able to deal with the concept or impact of Random Events (uncertainty). We therefore need to consider and factor in further novel and disruptive (systemic) approaches which offer us the possibility to manage uncertainty. We can do this by searching for, detecting and identifying Weak Signals – small, unexpected variations or disturbances in System outputs indicating hidden data within the general background System “noise” - which in turn may predicate the possible future existence or presence of emerging chaotic, and radically disruptive Wild Card or Black Swan events beginning to form on the detectable Horizon – or even just beyond. Random Events can then be factored into Complex Systems Modelling. Complex Systems interact with unseen forces – which in turn act to inject disorder, randomness, uncertainty, chaos and disruption. The Global Economy, and other Complex Adaptive Systems, may in future be considered and modelled successfully as a very large set of multiple interacting Ordered (Constrained) Complex Systems - each individual System loosely coupled with all of the others, and every System with its own clear set of rules and an ordered (restricted) number of elements and classes, relationships and types.

Randomness

Stochastic Processes – Random Events

• A tradition that begins with the classical Greek natural philosophers (circa 600 -

200 BC) and continues through contemporary science - holds that change and

the order of nature are the result of natural forces. What is the role of random,

stochastic processes in a universe that exhibits such order? When we examine

the heavens there seems to be a great deal of order to the appearance and

movement of the celestial bodies - galaxies, stars, planets, asteroids, etc.

• Since the dawn of our species, humans have speculated on how these bodies

were formed and on the meaning of their movements. Most observations of

natural phenomena support the contention that nature is ordered. The force

that brought about this order differs depending upon the source of the historic

explanation of how this order came to be. For most of human history, super-

natural forces were credited with the imposition of order on nature.

Randomness

Random Processes

• Random Processes may influence any natural and human phenomena, such as: -

– the history of an object

– the outcome of an event

– the execution of a process

• Randomness may be somewhat difficult to demonstrate, as true Randomness in chaotic

system behaviour is not always readily or easily distinguishable from any of the “noise”

that we may find in Complex Systems – such as foreground and background wave

harmonics, resonance and interference. Complex Systems may be influenced by both

internal and external factors which remain hidden – either unrecognised or unknown.

These hidden and unknown factors may exist far beyond our ability to detect them – but

nevertheless, still exert influence. The existence of weak internal or external forces acting

on systems may not be visible to the observer – these subliminal temporal forces can

influence Complex System behaviour in such a way that the presence of imperceptibly tiny

inputs, acting on a system, amplified in effect over many system cycles - are ultimately

able to create massive observable changes to outcomes in complex system behaviour.

Randomness


introduces into our daily lives. Research into stochastic (random) processes looks

towards how we might anticipate, prepare for and manage the chaos and uncertainty

which acts on complex systems – including natural systems such as Cosmology and

Climate, as well as human systems such as Politics and the Economy – so that we may

anticipate future change and prepare for it…..

1. Classical Mechanics - Any apparent randomness is as a result of Unknown Forces

2. Thermodynamics - Randomness, chaos and uncertainty is directly a result of Entropy

3. Biology - Any apparent randomness is as a result of Unknown Forces

4. Chemistry - Any apparent randomness is as a result of Unknown Forces

5. Atomic Theory - All events are utterly and unerringly predictable (Dirac Equation)

6. Quantum Mechanics - Every event is both symmetrical and random (Hawking Paradox)

7. Geology - Any randomness or asymmetry is a result of Unknown Forces

8. Astronomy - Any randomness or asymmetry is a result of Unknown Forces

9. Cosmology - Any randomness or asymmetry is as a result of Dark Matter, Energy, Flow

10. Relativity Theory - Randomness or asymmetry may be a result of Quantum effects

11. Wave Mechanics - Any randomness and asymmetry is as a result of Unknown Forces

Randomness


Classical Mechanics

(Newtonian Physics)



Sir Isaac Newton


Watt, Stephenson









Werner Heisenberg,

Paul Dirac,

Richard Feynman

Randomness









Celestial Objects









Celestial Objects

Hoyle, Ryall, Rees,






Albert Einstein,

Hermann Minkowski,

Stephen Hawking

Wave Mechanics

(String Theory or

Quantum Dynamics)

The Universe,

Membranes and

Hyperspace

Michael Green,

Michio Kaku

Randomness





• Thermodynamics

– governs the flow of energy and the transformation (change in state) of systems

– randomness, chaos and uncertainty is the result of the effects of Enthalpy and Entropy

• Chemistry

– Chemistry (Transformation) governs the change in state of atoms and molecules



• Biology

– Biology (Ecology ) governs Evolution - the life and death of all living Organisms



Randomness

• Atomic Theory

– governs the behaviour of unimaginably small objects (atoms and sub-atomic particles)

– all events are truly and intrinsically, utterly and unerringly predictable (Dirac Equation).


– governs the behaviour of unimaginably tiny objects (fundamental sub-atomic particles)


• Geology

– Geology governs the behaviour of local Solar System Objects (such as The Earth, Planets,

Planetoids, Asteroids, Meteors / Meteorites) which populate the Solar System


unmeasurable Unknown Forces - either internal or external - acting upon a System

• Astronomy

– Astronomy governs the behaviour of Common, Observable Celestial Objects (such as

Asteroids, Planets, Stars and Stellar Clusters) which populate and structure Galaxies



Randomness

• Cosmology

– Cosmology governs the behaviour of impossibly super-massive cosmic building blocks


– any apparent randomness or asymmetry is due to the influence of Quantum Effects,

Unknown Forces (Dark Matter, Dark Flow and Dark Energy) or Unknown Dimensions










Randomness

• If the movement of an object resulted from the operation of stochastic

processes, a repeating pattern of motion would not occur - and we would not

be able to predict with any accuracy the next location of the object as it move

down its path. Examples of stochastic processes include: - the translational

motion of atomic or molecular substances, such as the hydrogen ions in the

core of the sun; the outcomes from flipping a coin; etc. Stochastic processes

govern the outcome of games of chance – unless those games are “fixed”.

• Disruptive Future paradigms in Future Studies, when considered along with

Wave (String) Theory in Physics – alert us to the possibility of chaotic and

radically disruptive Random Events that generate ripples which propagate

outwards from the causal event like a wave – to flow across Space-Time.

Different waves might travel through the Time-Space continuum at slightly

different speeds due to the “viscosity” (granularity) in the substance of the

Space-Time Continuum (dark energy and dark matter).

Randomness

• Some types of Wave may thus be able to travel faster than others – either

because those types of Wave can propagate through Time-Space more rapidly

than other Wave types – or because certain types of Wave form can take

advantage of a “short cut” across a “warp” in the Time-Space continuum.

• A “warp” brings two discrete points from different Hyperspace Planes close

enough together to allow a Hyperspace Jump. Over any given time interval -

multiple Hyperspace Planes stack up on top of each other to create a time-line

which extends along the temporal axis of the Minkowski Space-Time Continuum.

• As we have discussed previously - Space (position) and Time (history) flow

inextricably together in a single direction – towards the future. In order to

demonstrate the principle properties of the Minkowski Space-Time continuum,

any type of Spatial and Temporal coupling in a Model or System must be able to

show over time that the History of a particle or the Transformation of a

process are fully and totally dependent on both its Spatial (positional) and

Temporal (historic) components acting together in unison.

Randomness

• Neither data-driven nor model-driven representations of the future are capable

alone, and by themselves, of dealing with the effects of chaos (uncertainty). We

therefore need to consider and factor in further novel and disruptive system

modelling approaches in order to help us to understand how Natural Systems

(Cosmology, Climate) and Human Activity Systems (Economics, Sociology)

perform. Random, Chaotic and Disruptive Wild Card or Black Swan events

may thus be factored into our System Models in order to account for uncertainty.

• Horizon Scanning, Tracking and Monitoring techniques offer us the possibility to

manage uncertainty by searching for, detecting and identifying Weak Signals –

which are messages from Random Events coming towards us from the future.

Faint seismic disturbances warn us of coming of Earth-quakes and Tsunamis.

Weak Signals (seismic disturbances) may often be followed by Strong Signals

(changes in topology), Wild Card (volcanic eruptions) or Black Swan (pyroclastic

cloud and ocean wave events), Horizon Scanning may help us to use Systems

Modelling to predict Natural Events like Earth-quakes and Tsunamis – as well as

Biological processes such as the future of Ecosystems, and Human Processes

such as the cyclic rise and fall of Commodity, Stocks and Shares market prices.

Data-driven v. Model-driven Domains Model-driven

Data-driven Rationalism


Reaction

Scepticism

Dogma

Enlightenment

Pragmatism

Realism

Social Sciences

Sociology

Economics





Life Sciences








Earth Sciences

Economic Analysis


Applied mathematics

Geography

Geology

Chemistry

Engineering


Archaeology

Palaeontology


Future Management




Astronomy

Cosmology

Relativity

Astrophysics

Astrology


Climate Change



Statistics

Strategic Foresight


Cluster Theory

Pure mathematics

Particle Physics

String Theory

Quantum Mechanics


Futures Studies


The Nature of Randomness – Uncertainty

• Randomness makes any precise prediction of future outcomes impossible.

We are unable to predict any future outcome with any significant degree of

confidence or accuracy – due to the inherent presence of uncertainty

associated with Complex Systems. Randomness in Complex Systems

introduces chaos and disorder – causing disruption. Events no longer continue

to unfold along a smooth, predictable linear course leading towards an

inevitable outcome – instead, we experience surprises.

• What we can do, however, is to identify the degree of uncertainty present in

those Systems, based on known, objective measures of System Order and

Complexity - the number and nature of elements present in the system, and the

number and nature of relationships which exist between those System

elements. This in turn enables us to describe the risk associated with possible,

probable and alternative Scenarios, and thus equips us to be able to forecast

risk and the probability of each of those future Scenarios materialising.

The Nature of Randomness – Uncertainty

• If true randomness exists and future outcomes cannot be predicted –

then what is the origin of that randomness? For example, are unexpected

outcomes simply apparent as a result of sub-atomic nano-randomness

existing at the quantum level – such as uncertainty phenomena etc…..?

• The Stephen Hawking Paradox postulates that uncertainty dominates complex

and chaotic systems to such an extent that future outcomes are both unknown -

and unknowable. The working context of this paradox is however, largely

restricted to the realm of Quantum Mechanics – where each and every natural

event that occurs at the sub-atomic level is truly completely and intrinsically –

both symmetrical and random.


Simplexity Ordered

Complexity

Disordered


Systems (CAS)

Linear

Systems


Chaos Order


• What is the explanation for randomness evident in all high-order

phenomena found in nature…?



limited Systemic Models which attempt to simplify, abstract and simulate

reality - must be clearly distinguished between and understood.

• When we design our Systemic Models representing Actual Reality – such as

the Economy, Geo-political systems, Climate Change, Weather and so on - if

we are lucky enough, then some high-order phenomena found in nature may

be captured by a random rule; and with even more luck, by a deterministic rule

(which can be regarded as a special case of randomness) - but if we are

unlucky - then none of those rules might be captured at all. Regarding the

nature of reality - it still remains unclear what factors distinguish those truly

random phenomenon found in nature at the Quantum level (e.g. radioactive

decay?) from other Random Events - which are triggered by unseen forces.


• Can we accept that these natural phenomena are not truly random at all

– that is, given sufficient information such as complete event data sets -

it is possible to predict random events? If so, are all random events the

result of the same natural phenomenon - unseen or hidden forces ? “

• Classical Mechanics (Newtonian Physics) describe the laws which govern all

of the systems and objects that we are familiar with in our everyday routine

lives. Relativity Theory, on the other hand, describes unimaginably large

things, whilst Quantum Mechanics describes impossibly small things – and

Wave Mechanics (String Theory) attempts to describe everything. True

randomness does not really exist in Classical (Newtonian) Physics – the laws

which control Chaos and Complex Systems that govern every aspect of our

life on Earth today – from Natural Systems such as Cosmology, Astronomy,

Climatology, Geology and Biology through to Human Activity Systems such

as Political, Economic and Sociological Complex Adaptive Systems (CAS).


• Randomness is simply the results of those forces which are not known, not recognised, not understood, are not under the control of the observer or simply occur outside of the known boundaries of observable system components – but, nevertheless, must still exist and exert influence over the system. Over many System Cycles, immeasurably small inputs interacting with Complex System components and relationships - may be amplified into extremely significant outputs.....

1. Classical (Newtonian) Physics – which governs all of the everyday events around us – where apparent randomness is as a result of Unknown Forces

2. Relativity Theory – which governs the events of impossibly large objects – any apparent randomness or asymmetry is as a result of Quantum effects

3. Quantum Mechanics – which governs the events of unimaginably small objects – all events are truly and intrinsically both symmetrical and random

4. Wave (String) Theory – which attempts to integrate the behaviour of everyday objects, impossibly large objects and unimaginably small objects – apparent randomness and asymmetry is as a result of Unknown Forces – which may in turn have their origination in Quantum Dynamics effects

Randomness

Black Swan – Nassim Taleb

• Black Swan by Nassim Taleb was first published in 2007 and quickly sold out, with close

to 3 million copies purchased as of February 2011. Fooled by Randomness and Black

Swan seized the public imagination, and quickly generated mass-market interest to create

a new, niche market segment for Future Management publications - which cross-over

General Interest, Professional and Academic sectors. Taleb's non-technical writing style

mixes a narrative text (often semi-autobiographical) and whimsical home-spun tales

backed up by some historical and scientific content. The success of Taleb's first two books

(Fooled by Randomness and the Black Swan) gained him an advance on Royalties of

$4 million for his follow-up book – the Blank Swan.

The Drunkard's Walk:- How Randomness Rules Our Lives - Leonard Mlodinow

• The Drunkard's Walk dives deeper into Randomness. This book is different - it is natural

for scientific books to discuss science – but unusual for them to contain highly readable

prose and good humour, not to mention useful and practical insights which help to live your

life with a greater understanding of the world about you. The book's major weakness is

that it comes up short on fundamental explanations of Chaos, Disruption, Complexity and

Randomness. Mlodinow simply advises readers to "be aware" and "conscious" of how

important randomness is.

Randomness –The Drunkards Walk

• Randomness The

Drunkards Walk – is

the motion of a moving

body subject to random

changes in direction

• This pattern is

sometimes referred to

as “the drunkard's walk”.

The intersecting lines at

the top and the right of

the picture are

Cartesian coordinates

and mark the origin

where X=0 and Y=0.

• The actual random walk

is long and torturous,

but the actual vector

distance travelled from

the point of origin cross

hairs (0,0) is very short.


• Disruptive Future paradigms in Future Studies along with Wave Theory (String

Theory) in Physics - alert us to the phenomenon of chaotic and radically disruptive

Random Events which can generate Temporal Disturbances in the Space–Time

Continuum – waves which propagate out like a ripple and travel outwards from

that Random Event - through the Space-Time Continuum.

• Weak Signals, Strong Signals, Wild Cards and Black Swan Events – are a

sequence of linked and integrated waves in ascending order of magnitude, which

have a common source or origin - either a single Random Event instance or

arising from a linked series of chaotic and disruptive Random Events - an Event

Storm. These Random Events propagate through the space-time continuum as a

related and integrated series of waves with an ascending order of magnitude and

impact – the first wave to arrive is the fastest travelling,- Weak Signals - something

like a faint echo of the causal Random Event, This may in turn be followed in turn

by a ripple (Strong Signals) then possibly by a wave (Wild Card) - which could

indicate the unfolding a further increase in magnitude and intensity which suddenly

and catastrophically arrives - something like a tsunami (Black Swan Event).


• Random Events may essentially “bend” the Time-Space continuum. Some Waves

may thus be able to travel faster than others – either because certain Wave form

types can propagate through Time-Space more rapidly than other Wave types - or

because certain types of Wave take a “short cut” as a “bend” in the Time-Space

continuum brings two discrete points closer together from different Hyperspace

Planes. Over a time interval multiple Hyperspace Planes stacked up on top of each

other create a time-line extending along the Time axis of the Minkowski Space-Time

Continuum.


1. Unseen Forces 1. Discovery

2. Random Event 1.1 Establishment

3. Weak Signals 1.2 Development

4. Strong Signals 2. Growth

5. Wild Cards 3. Plateau

6. Black Swan Event 4. Decline

5. Collapse

5.1 Renewal

5.2 Replacement


• Randomness. Weak Signals, Wild Cards and Black Swan Events – may be

evidence of a chain of radically disruptive and chaotic Random Events which are

due to the action of unseen forces – that propagate through the Space-Time

Continuum in the same way as a ripple becomes a wave and crosses the ocean.

• Perhaps some of the different Wave Types - Weak Signals, Wild Cards and

Black Swan Events can travel faster or take a different route compared with

some of the other types – perhaps because their Wave forms can propagate

through the Space- Time Matrix (which is made up of dark matter, dark energy

and dark flow) more rapidly than the other Wave forms - or perhaps they are

different types of Wave – and specific Wave Types may able to take a “short-cut”

between two points on different Hyperspace Planes – and so arrive sooner.

• It is possible that certain types of Random Event may be able to “bend” the Time-

Space continuum – to bring two discrete points on different Hyperspace Planes

closer together and so take a short-cut over a time interval extended through a

time-line flowing along the Time axis of the Minkowski Space-Time Continuum.


• Every item of Global Content that we find in the Present is somehow

connected with both the Past and the Future. Space-Time is a Dimension –

which flows in a single direction, as does a River – towards the Future.

• Space-Time, like water diverted along an alternative river channel, does not

always flow uniformly – outside of the main channel there could well be

“submerged objects” (random events) that disturb the passage of time, and

may possess the potential capability of creating unforeseen eddies, whirlpools

and currents in the flow of Time (disorder and uncertainty) – which in turn

posses the capacity to generate ripples, and waves (chaos and disruption) –

thus changing the course of the Time-Space continuum. “Weak Signals” are

“Ghosts in the Machine” of these subliminal temporal interactions – with the

capability to contain information about future “Wild card” or “Black Swan”

random events.

Complex Systems and Chaos Theory

Complex Systems and Chaos Theory has been used extensively in the field of Futures Studies, Strategic

Management, Natural Sciences and Behavioural Science. It is applied in these domains to understand

how individuals within populations, societies, economies and states act as a collection of loosely

coupled interacting systems which adapt to changing environmental factors and random events – bio-ecological, socio-economic or geo-political.....

Linear and Non-linear Systems

Linear Systems – all system outputs are directly and proportionally related to system inputs

• Types of linear algebraic function behaviours; examples of Simple Systems include: -

– Game Theory and Lanchester Theory

– Civilisations and SIM City Games

– Drake Equation (SETI) for Galactic Civilisations

Non-linear Systems – system outputs are asymmetric and not proportional or related to inputs

• Types of non-linear algebraic function behaviours: examples of Complex / Chaotic Systems are: -

– Complex Systems – large numbers of elements with both symmetric and asymmetric relationships

– Complex Adaptive Systems (CAS) – co-dependency and co-evolution with external systems

– Multi-stability – alternates between multiple exclusive states.(lift status = going up, down, static)

– Chaotic Systems

• Classical chaos – the behaviour of a chaotic system cannot be predicted.

• A-periodic oscillations – functions that do not repeat values after a certain period (# of cycles)

– Solitons – self-reinforcing solitary waves - due to feedback by forces within the same system

– Amplitude death – any oscillations present in the system cease after a certain period (# of cycles)

due to feedback by forces in the same system - or some kind of interaction with external systems.

– Navis-Stokes Equation for the motion of a fluid: -

• Weather Forecasting

• Plate Tectonics and Continental Drift

http://en.wikipedia.org/wiki/Classical_chaos

http://en.wikipedia.org/wiki/Aperiodic



http://en.wikipedia.org/wiki/Soliton

http://en.wikipedia.org/wiki/Amplitude_death


• System Complexity is typically characterised and measured by the number of elements in a

system, the number of interactions between elements and the nature (type) of interactions.

• One of the problems in addressing complexity issues has always been distinguishing between

the large number of elements (components) and relationships (interactions) evident in chaotic

(unconstrained) systems - Chaos Theory - and the still large, but significantly smaller number

of both and elements and interactions found in ordered (constrained) Complex Systems.

• Orderly System Frameworks tend to dramatically reduce the total number of elements and

interactions – with fewer and smaller classes of more uniform elements – and with reduced,

sparser regimes of more restricted relationships featuring more highly-ordered, better internally

correlated and constrained interactions – as compared with Disorderly System Frameworks.


Simplexity Ordered

Complexity

Disordered


Systems (CAS)

Linear

Systems


Chaos Order

System Complexity

• System Complexity is typically characterised by the number of elements in a system,

the number of interactions between those elements and the nature (type) of interactions.

One of the problems in addressing complexity issues has always been distinguishing

between the large number of elements and relationships, or interactions evident in

chaotic (disruptive, unconstrained) systems - and the still large, but significantly smaller

number of elements and interactions found in ordered (constrained) systems.

• Orderly (constrained) System Frameworks tend to have both a restricted number of

uniform elements with simple (linear, proportional, symmetric) interactions with just a few

element and interaction classes of small size, featuring explicit interaction rules which

govern more highly-ordered, internally correlated and constrained interactions – and

therefore tend to exhibit predictable system behaviour with smooth, linear outcomes.

• Disorderly (unconstrained) System Frameworks – tend to have both a very large total

number of non-uniform elements featuring complex (non-linear, asymmetric) interactions

which may be organised into many classes and regimes. Disorderly (unconstrained)

System Frameworks – feature a greater number of more disordered, uncorrelated and

unconstrained element interactions with implicit or random rules – which tend to exhibit

unpredictable, random, chaotic and disruptive system behaviour – and creates surprises.

Complexity Map


• A system may be defined as simple or linear whenever its evolution sensitively is fully

independent of its initial conditions – and may also be described as deterministic

whenever the behaviour of a simple (linear) systems can be accurately predicted and

when all of the observable system outputs are directly and proportionally related to

system inputs. We can expect smooth, linear, highly predictable outcomes to simple

systems which are driven by linear algebraic functions.

• A system may be described as chaotic whenever the system evolution sensitively is

fully dependant upon its initial conditions – and may also be defined as probabilistic –

whenever the behaviour of that stochastic system cannot be predicted. This property

of dependency on initial conditions in chaotic systems implies that from any two invisibly

different starting points or variations in starting conditions – then their trajectories begin

to diverge – and the degree of separation between the two trajectories increases

exponentially over the course of time. In this way, over numerous System Cycles –

invisibly small differences in initial conditions are amplified until they become radically

divergent, eventually producing totally unexpected results with unpredictable outcomes.

Instead of smooth, linear outcomes – we experience surprises. This is why complex,

chaotic systems such as weather and the economy – are impossible to accurately

predict. What we can do, however, is to describe possible, probable and alternative

future scenarios – and calculate the probability of each of those scenarios materialising.


• Chaos Theory has been used extensively in the fields of Futures Studies, Natural

Sciences, Behavioural Science, Strategic Management, Threat Analysis and Risk

Management. The requirements for a stochastic system to become chaotic, are that the

system must be non-linear and multi-dimensional – that is, the system posses at least

three dimensions. The Space-Time Continuum is already multi-dimensional – so any

complex (non-linear) and time-variant system which exists over time in three-dimensional

space - meets all of these criteria.

• The Control of Chaos refers to a process where a tiny external system influence is

applied to a chaotic system, so as to slightly vary system conditions – in order to achieve

a desirable and predictable (periodic or stationary) outcome. To synchronise and resolve

chaotic system behaviour we may invoke external procedures for stabilizing chaos which

interact with symbolic sequences of an embedded chaotic attractor - thus influencing

chaotic trajectories. The major concepts involved in the Control of Chaos, are described

by two methods – the Ott-Grebogi-Yorke (OGY) Method and the Adaptive Method.

• The Adaptive Method for the resolution of Complex, Chaotic Systems introduces multiple

relatively simple and loosely coupled interacting systems in an attempt to model over time

the behaviour of a single, large Complex and Chaotic System - which may still be subject

to undetermined external influences – thus creating random system effects.....

Wave-form Analytics in Cycles

• Wave-form Analytics is a powerful new analytical tool “borrowed” from spectral

wave frequency analysis in Physics – which is based on Time-frequency analysis –

a technique which exploits the wave frequency and time symmetry principle. This is

introduced here for the first time in the study of natural and human activity waves,

and in the field of economic cycles, business cycles, market patterns and trends.

• Trend-cycle decomposition is a critical technique for testing the validity of multiple

(compound) dynamic wave-form models competing in a complex array of

interacting and inter-dependant cyclic systems in the study of complex cyclic

phenomena - driven by both deterministic and stochastic (probabilistic) paradigms.

In order to study complex periodic economic phenomena there are a number of

competing analytic paradigms – which are driven by either deterministic methods

(goal-seeking - testing the validity of a range of explicit / pre-determined / pre-

selected cycle periodicity value) and stochastic (random / probabilistic / implicit -

testing every possible wave periodicity value - or by identifying actual wave

periodicity values from the “noise” – harmonic resonance and interference patterns).


• A fundamental challenge found everywhere in business cycle theory is how to

interpret very large scale / long period compound-wave (polyphonic) time series data

sets which are dynamic (non-stationary) in nature. Wave-form Analytics is a new

analytical too based on Time-frequency analysis – a technique which exploits the

wave frequency and time symmetry principle. The role of time scale and preferred

reference from economic observation are fundamental constraints for Friedman's

rational arbitrageurs - and will be re-examined from the viewpoint of information

ambiguity and dynamic instability.

• The Wigner-Gabor-Qian (WGQ) spectrogram demonstrates a distinct capability for

revealing multiple and complex superimposed cycles or waves within dynamic, noisy

and chaotic time-series data sets. A variety of competing deterministic and

stochastic methods, including the first difference (FD) and Hodrick-Prescott (HP)

filter - may be deployed with the multiple-frequency mixed case of overlaid cycles

and system noise. The FD filter does not produce a clear picture of business cycles

– however, the HP filter provides us with strong results for pattern recognition of

multiple co-impacting business cycles. The existence of stable characteristic

frequencies in large economic data aggregations (“Big Data”) provides us with strong

evidence and valuable information about the structure of Business Cycles.


Wave-form Analytics in Natural Cycles

• Solar, Oceanic and Atmospheric Climate Forcing systems demonstrate Complex Adaptive

System (CAS) behaviour – behaviour which is more similar to an organism than that of

random and chaotic “Stochastic” systems. The remarkable long-term stability and

sustainability of cyclic climatic systems contrasted with random and chaotic short-term

weather systems are demonstrated by the metronomic regularity of climate pattern

changes driven by Milankovich Solar Cycles along with 1470-year Dansgaard-Oeschger

and Bond Cycles – regular and predictable and Oceanic Forcing Climate Sub-systems.

Wave-form Analytics in Human Activity Cycles

• Economic systems also demonstrate Complex Adaptive System (CAS) behaviour - more

similar to an ecology than chaotic “Random” systems. The capacity of market economies

for cyclic “boom and bust” – financial crashes and recovery - can be seen from the impact

of Black Swan Events causing stock market crashes - such as the failure of sovereign

states (Portugal, Ireland, Greece, Iceland, Italy and Spain) and market participants

(Lehman Brothers) due to oil price shocks, money supply shocks and credit crises.

Surprising pattern changes occurred during wars, arm races, and during the Reagan

administration. Like microscopy for biology, non-stationary time series analysis opens up

a new space for business cycle studies and policy diagnostics.

Complex Adaptive Systems Adaption and Evolution

When Systems demonstrate properties of Complex

Adaptive Systems (CAS) - often defined as a

collection or set of relatively simple and loosely

connected interacting systems exhibiting co-adapting

and co-evolving behaviour - then those systems are

much more likely to adapt successfully to their

environment and, thus better survive the impact of both

gradual change and of sudden random events.

Complex Adaptive Systems

• Complex Adaptive Systems (CAS) and Chaos Theory has also been

used extensively in the field of Futures Studies, Strategic Management,

Natural Sciences and Behavioural Science. It is applied in these domains

to understand how individuals within populations, societies, economies and

states act as a collection of loosely coupled interacting systems which

adapt to changing environmental factors and random events – biological,

ecological, socio-economic or geo-political.

• Complex Adaptive Systems (CAS) and Chaos Theory treats individuals,

crowds and populations as a collective of pervasive social structures which

may be influenced by random individual behaviours – such as flocks of

birds moving together in flight to avoid collision, shoals of fish forming a

“bait ball” in response to predation, or groups of individuals coordinating

their behaviour in order to respond to external stimuli – the threat of

predation or aggression – or in order to exploit novel and unexpected

opportunities which have been discovered or presented to them.


• When Systems demonstrate properties of Complex Adaptive Systems (CAS) - which is

often defined as a collection or set of relatively simple and loosely connected interacting

systems exhibiting co-adapting and co-evolving behaviour (sub-systems or components

changing together in response to the same external stimuli) - then those systems are

much more likely to adapt successfully to their environment and, thus better survive the

impact of both gradual change and of sudden random events. Complexity Theory

thinking has been present in biological, strategic and organisational system studies since

the first inception of Complex Adaptive Systems (CAS) as an academic discipline.

• Complex Adaptive Systems are further contrasted compared with other ordered and

chaotic systems by the relationship that exists between the system and the agents and

catalysts of change which act upon it. In an ordered system the level of constraint means

that all agent behaviour is limited to the rules of the system. In a chaotic system these

agents are unconstrained and are capable of random events, uncertainty and disruption.

In a CAS, both the system and the agents co-evolve together; the system acting to

lightly constrain the agents behaviour - the agents of change, however, modify the

system by their interaction. CAS approaches to behavioural science seek to understand

both the nature of system constraints and change agent interactions and generally takes

an evolutionary or naturalistic approach to crowd scenario planning and impact analysis.


• Biological, Sociological, Economic and Political systems all tend to demonstrate

Complex Adaptive System (CAS) behaviour - which appears to be more similar

in nature to biological behaviour in an population than to truly Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable long-term

adaptability, stability and resilience of market economies may be demonstrated by

the impact of Black Swan Events causing stock market crashes - such as oil price

shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards) – by

the ability of Financial markets to rapidly absorb and recover from these events.

• Unexpected and surprising Cycle Pattern changes have historically occurred during

regional and global conflicts being fuelled by technology innovation-driven arms

races - and also during US Republican administrations (Reagan and Bush - why?).

Just as advances in electron microscopy have revolutionised the science of biology

- non-stationary time series wave-form analysis has opened up a new space for

Biological, Sociological, Economic and Political system studies and diagnostics.

Crowd Behaviour – the Swarm

• In a crowd of human beings or a swarm of animals, individuals are so closely connected that they share the same mood and emotions (fear, greed, rage) and demonstrate the same or very similar behaviour (fight, flee or feeding frenzy). Only the initial few individuals exposed to the Causal Event or incident may at first respond strongly and directly to the initial “trigger” stimulus, causal event or incident (opportunity or threat – such as external predation, aggression or discovery of a novel or unexpected opportunity to satisfy a basic need – such as feeding, reproduction or territorialism).

• Those individuals who have been directly exposed to the initial “trigger” event or incident - the system input or causal event that initiated a specific outbreak of behaviour in a crowd or swarm – quickly communicate and propagate their swarm response mechanism and share with all the other individuals – those members of the Crowd immediately next to them – so that modified Crowd behaviour quickly spreads from the periphery or edge of the Crowd.

• Peripheral Crowd members in turn adopt the Crowd response behaviour without having been directly exposed to the “trigger”. Most members of the crowd or swarm may be totally oblivious as to the initial source or nature of the trigger stimulus - nonetheless, the common Crowd behaviour response quickly spreads to all of the individuals in or around that crowd or swarm.


• An example of Random Clustering is a Crowd or Swarm. There are a various forces

which contribute towards Crowd Behaviour – or Swarming. In any crowd of human

beings or a swarm of animals, individuals in the crowd or swarm are closely connected

so that they share the same mood and emotions (fear, greed, rage) and demonstrate

the same or very similar behaviour (fight, flee or feeding frenzy). Only the initial few

individuals exposed to the Random Event or incident may at first respond strongly and

directly to the initial “trigger” stimulus, causal event or incident (opportunity or threat –

such as external predation, aggression or discovery of a novel or unexpected

opportunity to satisfy a basic need – such as feeding, reproduction or territorialism).

• Those individuals who have been directly exposed to the initial “trigger” event or incident -

the system input or causal event that initiated a specific outbreak of behaviour in a crowd or

swarm – quickly communicate and propagate their swarm response mechanism and share

with all the other individuals – those members of the Crowd immediately next to them – so

that modified Crowd behaviour quickly spreads from the periphery or edge of the Crowd.

• Peripheral Crowd members in turn adopt Crowd response behaviour without having been

directly exposed to the “trigger”. Members of the crowd or swarm may be oblivious to the

initial source or nature of the trigger stimulus - nonetheless, the common Crowd behaviour

response quickly spreads to all of the individuals in or around that core crowd or swarm.


• One of the dangers posed by human crowd behaviour is that of “de-individualisation” in a

crowd, where a group of random individuals aggregate together and begin acting in concert

- adopting common behaviour, aims and objectives – and may begin to exhibit uninhibited

crowd responses to external information and stimuli. Crowd participants in this state begin

to respond without the usual constraints of their normal social, ethical, moral, religious and

behavioural rules. These are the set of circumstances which led to events such as the Arab

Spring and London Riots - which spread rapidly through deprived communities across the

country, both urban and rural. This type of collective group behaviour – such as a “feeding

frenzy” – has been observed in primates and carnivores - and even in rodents and fish.....

• Crowd behaviour is not just the domain of Demonstrators and Protesters - it can also be

seen in failing economies with the actions of Economic Planners in Central Banks - along

with their Political Masters – who also behave as a group of individuals acting together in

concert without the usual constraints – and thus, under extreme psychological stress as

systems such as the economy begins to collapse unpredictably – start to demonstrate "de-

individualisation" - collective uninhibited responses to external information and stimuli,

without the constraints of their normal political, economic, social, ethical, moral and

behavioural rules. These circumstances may lead to further panic and crowd behaviour

across Towns and Cities, Banks and Financial Institutions, ultimately Municipal, State and

Federal government departments - causing the failure of Global Markets or the fall of

Governments – as was recently witnessed in both the Arab Spring and the Euro Crisis.

Moore's Law

Moore's Law

• In 1965, the observation made by Gordon Moore, co-founder of Intel, is that the number

of transistors per square inch on integrated circuits had doubled every year since the integrated

circuit was invented. Moore predicted that this trend would continue for the foreseeable future. In

subsequent years, the pace of change has slowed down somewhat - but Data Storage Density

(gigabytes) has doubled approximately every 18 months - a definition which Moore himself has

blessed. The current definition of Moore's Law, accepted by most experts, including Disruptive

Futurists and Moore himself, is that Computing Power (gigaflops) will double about every two

years. Expect Moore's Law to hold good for at least another generation.....

• A forecast - and a challenge. Gordon Moore’s forecast for the pace of change in silicon

technology innovation - known as Moore's Law - essentially describes the basic business model

for the semiconductor industry. Intel, through investments in technology and manufacturing has

made Moore’s Law a reality. As transistor scale gets ever smaller Intel expects to continue to

deliver on Moore’s prediction well into the foreseeable future by using an entirely new transistor

formula that alleviates wasteful electricity leaks creating more energy-efficient processors.

• Exponential growth that continues today. Continuing Moore's Law means the rate of

progress in the semiconductor industry will far surpass that of nearly all other industries. The

future of Moore’s Law could deliver a magnitude of exponential capability increases, driving a

fundamental shift in computing, networking, storage, and communication devices to handle the

ever-growing digital content and Intel's vision of 15 billion intelligent, connected smart devices.

http://www.webopedia.com/TERM/I/Intel.html

http://www.webopedia.com/TERM/T/transistor.html

http://www.webopedia.com/TERM/I/integrated_circuit_IC.html

Forecasting and Predictive Analytics

• ECONOMIC MODELLING and LONG-RANGE FORECASTING •

• Economic Modelling and Long-range Forecasting is driven by atomic Data Warehouse

Structures and sophisticated Economic Models containing both Historic (up to 200 years daily

closing prices for Commodities, shares and bonds) and Future values (daily forecast and weekly

projected price curves, monthly and quarterly movement predictions, and so on for up to 50

years into the future – giving a total timeline of up to 250 years (Historic + 50 years Future trends

summary, outline movements and highlights). Forecast results are obtained using Economic

Models - Quantitative (technical) Analysis (Monte Carlo Simulation, Pattern and Trend Analysis -

Economic Growth and Recession / Depression shapes and Commodity Price Data Sets) in order

to construct a continuous 100 year “window” into Commodity Price Curves and Business Cycles

for Cluster Analysis and Causal Layer Analysis (CLA) – which in turn is used for driving out

Qualitative (narrative) Scenario Planning and Impact Analysis for describing future narrative epic

stories, scenarios and use-cases.

• PREDICTIVE ANALYITICS and EVENT FORECASTING •

• Predictive Analytics and Event Forecasting uses Horizon Scanning, Tracking and Monitoring

methods combined with Cycle, Pattern and Trend Analysis techniques for Event Forecasting and

Propensity Models in order to anticipate a wide range of business. economic, social and political

Future Events – ranging from micro-economic Market phenomena such as forecasting Market

Sentiment and Price Curve movements - to large-scale macro-economic Fiscal phenomena

using Weak Signal processing to predict future Wild Card and Black Swan Events - such as

Monetary System shocks.


• MARKET RISK •

Market Risk = Market Sentiment – Actual Results (Reality)

• The two Mood States – “Greed and Fear” are primitive human instincts which, until now, we've

struggled to accurately qualify and quantify. Social Networks, such as Twitter and Facebook,

burst on to the scene five years ago and have since grown into internet giants. Facebook has

over 900 million active members and Twitter over 250 million, with users posting over 2 billion

"tweets“ or messages every week. This provides hugely valuable and rich insights into how

Market Sentiment and Market Risk are impacting on Share Support / Resistance Price Levels –

and so is also a source of real-time data that can be “mined” by super-fast computers to forecast

changes to Commodity Price Curves

• STRATEGIC FORESIGHT •

• Strategic Foresight is the ability to create and maintain a high-quality, coherent and functional

forward view, and to utilise Future Insights in order to gain Competitive Advantage - for example

to identify and understand emerging opportunities and threats, to manage risk, to inform

planning and forecasting and to shape strategy development. Strategic Foresight is a fusion of

Foresight techniques with Strategy Analysis methods – and so is of great value in detecting

adverse conditions, threat assessment, guiding policy and strategic decision-modelling, in

identifying and exploring novel opportunities presented by emerging technologies, in evaluating

new markets, products and services and in driving transformation and change.


• INNOVATION •

• Technology Innovation is simply combining existing resources in new and different ways –

in order to create novel and innovative Products and Services. Understanding the impact

of Technology Convergence is the Key to driving Innovation. Many common and familiar

objects in use today exist only as a result of technology convergence? Your average,

everyday passenger vehicle or laptop computer is the culmination of a series of technology

consolidation and integration events from a large number of apparently unrelated

technological innovations and advancements. Light-weight batteries were developed to

provide independence from fixed power sockets and hard-disk drives were made compact

enough to be installed in portable devices. The smart phone and tablet resulted from a

further convergence of technologies such as cellular telecommunications, mobile internet,

and Smart Apps - mini-applications that do not need an on-board hard-disk drive.

• FUTURE MANAGEMENT •

• Providing future analysis and strategic advice to stakeholders so that they might

understanding how the Future may unfold - in order to anticipate, prepare for and manage

the Future, to resolve challenging business problems, to envision, architect, design and

deliver novel solutions in support of major technology refreshment and business

transformation programmes • Future Analysis • Innovation • Strategic Planning •

Business Transformation • Technology Refreshment •


. • GEO-DEMOGRAPHICS •

• The profiling and analysis of large aggregated datasets in order to determine a ‘natural’ or

implicit structure of data relationships or groupings where no prior assumptions are made

concerning the number or type of groups discovered or group relationships, hierarchies or

internal data structures - in order to discover hidden data relationships - is an important starting

point forming the basis of many statistical and analytic applications. The subsequent explicit

Cluster Analysis as of discovered data relationships is a critical technique which attempts to

explain the nature, cause and effect of those implicit profile similarities or geographic

distributions. Geo-demographic techniques are frequently used in order to profile and segment

populations by ‘natural’ groupings - such as common behavioural traits, Clinical Trial, Morbidity

or Actuarial outcomes, along with many other shared characteristics and common factors –and

then attempt to understand and explain those natural group affinities and geographical

distributions using methods such as Causal Layer Analysis (CLA).....

• Social Media is the fastest growing category of user-provided global content and will eventually

grow to 20% of all internet content. Gartner defines social media content as unstructured data

created, edited and published by users on external platforms including Facebook, MySpace,

LinkedIn, Twitter, Xing, YouTube and a myriad of other social networking platforms - in addition

to internal Corporate Wikis, special interest group blogs, communications and collaboration

platforms. Social Mapping is the method used to describe how social linkage between

individuals defines Social Networks and to understand the nature and dynamics of intimate

relationships between individuals


• GIS MAPPING and SPATIAL DATA ANALYSIS • • A Geographic Information System (GIS) integrates hardware, software, and

data capture devices for acquiring, managing, analysing, distributing and displaying all forms of geographically dependant location data – including machine generated data such as Computer-aided Design (CAD) data from land and building surveys, Global Positioning System (GPS) terrestrial location data - as well as all kinds of aerial and satellite image data.

• Spatial Data Analysis is a set of techniques for analysing spatial (Geographic) location data. The results of spatial analysis are dependent on the locations of the objects being analysed. Software that implements spatial analysis techniques requires access to both the locations of objects and their physical attributes. Spatial statistics extends traditional statistics to support the analysis of geographic data. Spatial Data Analysis provides techniques to describe the distribution of data in the geographic space (descriptive spatial statistics), analyse the spatial patterns of the data (spatial pattern or cluster analysis), identify and measure spatial relationships (spatial regression), and create a surface from sampled data (spatial interpolation, usually categorized as geo-statistics).


• “BIG DATA” •

• “Big Data” refers to vast aggregations (super sets) of individual datasets whose size and

scope is beyond the capability of conventional transactional Database Management

Systems and Enterprise Software Tools to capture, store, analyse and manage. Examples

of Big Data include the vast and ever changing amounts of data generated in social

networks where we have (unstructured) conversations with each other, news data streams,

geo-demographic data, internet search and browser logs, as well as the ever-growing

amount of machine data generated by pervasive smart devices - monitors, sensors and

detectors in the environment – captured via the Smart Grid, then processed in the Cloud –

and delivered to end-user Smart Phones and Tablets via Intelligent Agents and Alerts.

• Data Set Mashing and “Big Data” Global Content Analysis – supports Horizon Scanning,

Monitoring and Tracking activities by taking numerous, apparently un-related RSS and

other Information Streams and Data Feeds, loading them into Very large Scale (VLS) DWH

Structures and Document Management Systems for Real-time Analytics – searching for

and identifying possible signs of relationships hidden in data (Facts/Events)– in order to

discover and interpret previously unknown “Weak Signals” indicating emerging and

developing Application Scenarios, Patterns and Trends - in turn predicating possible,

probable and alternative global transformations unfolding as future “Wild Card” or “Black

Swan” events.


• WAVE-FORM ANAYITICS in “BIG DATA” •

• Wave-form Analytics help identify Cycles, Patterns and Trends in Big Data – characterised as

a sequence of high and low activity in time-series data – resulting in periodic increased and

reduced phases in regular, recurring cyclic trends. This approach supports an integrated study

of the impact of multiple concurrent cycles - and no longer requires iterative and repetitive

processes of trend estimation and elimination from the background “noise”.

• FORENSIC “BIG DATA” •

• Social Media Content and Spatial Mapping Data is used in order to understand intimate

personal relationships between individuals and to identify, locate and describe their participation

in various Global Social Networks. Thus the identification, composition, monitoring, tracking

,activity and traffic analysis of Social Networks Criminal Enterprises and Terrorist Cells – as

defined by common locations, business connections, social links and inter-personal

relationships – is used by Businesses to drive Influencer Programmes and by Government for

National Security, Counter-Terrorism, Anti-Trafficking, Criminal Investigation and Fraud

Prevention purposes.....

• Forensic “Big Data” combines the use of Social Media and Social Mapping Data in order to

understand intimate inter-personal relationships for the purpose of National Security, anti-

Trafficking and Fraud Prevention – through the identification, composition, activity analysis and

monitoring of Criminal Enterprises and Terrorist Cells.....

Random Event Clustering

Patterns in the Chaos

Random Event Clustering Patterns in the Chaos

The Nature of Uncertainty – Randomness Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects

– any apparent randomness is as a result of Unknown Forces

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles – all events are truly and intrinsically both symmetrical and random

Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures – any apparent randomness or asymmetry is as a result of Quantum Dynamics

Wave Mechanics (String Theory) – integrates the behaviour of every size & type of object – apparent randomness and asymmetry is as a result of Quantum and Unknown Forces

Clustering of co-impacting Events

• It is the function of every Futures Researcher or Disruptive Futurist to seek out and

discover a combination, sequence or chain of apparently Random Events which

occur together in Time and Space as an integrated sequence (groups or clusters) of

linked events. Random Event Clusters interacting together demonstrate transient or

instantiated relationships or dependencies (are co-related or dependant) – that is,

they are influencing each other in some way or another. These hidden relationships

show up in the values of data items (variables) in the data stack when we apply

systems modelling techniques (to discover explicit data relationships) or “Big Data”

methods (to discover implicit data relationships) in order to resolve Future Domain

opportunities or threats, risks, challenges, issues or problems .

• What factors or forces do we need to consider as being in-scope and critical to the

overall behaviour of the system? Which other factors or forces have we ignored,

overlooked or simply failed to consider? What further unknown factors or unseen

forces are out there which we have not detected – but still exist – which are

somehow impacting upon the behaviour and outcomes of the observable system -

thus exerting minute but critical influence on system elements (objects and their

interactions ) through Space and Time?


• The discovery of Chaos and Complexity has increased our understanding of the

Cosmos and its effect on us. If you surf the chaos content regions of the internet,

you will invariably encounter terms such as: -

• These influences can take some time to manifest themselves, but that is the nature

of the phenomena identified as a "strange attractor." Such differences could be

small to the point of invisibility - how tiny can influences be to have any effect?

This is captured in the “butterfly effect” scenario which is described later.

1. Attraction 14. Phase space and locking

2. Chaos 3. Clustering 4. Complexity 5. Butterfly effect 6. Disruption 7. Dependence 8. Feedback loops 9. Fractal patterns and dimensions 10. Harmonic Resonance 11. Horizon of predictability 12. Interference patterns 13. Massively diverse outcomes

15. Randomness 16. Repellence 17. Sensitivity to conditions 18. Self similarity (self affinity) 19. Starting conditions 20. Stochastic events 21. Strange attractors 22. System cycles (iterations) 23. Time-series Events 24. Turbulence 25. Uncertainty 26. Vanishingly small differences

http://syzygyastro.hubpages.com/hub/Defining-the-Fractal


• Nothing in the galaxy, in our world, or in our own personal existence, ever

happens in isolation of other places, objects, individuals and events. This is a

very simple and fundamental fact about life, nature the universe and everything.

In precisely the same moment as one event occurs or one transformation

happens in one location, infinite other events are taking place simultaneously in

countless other locations - which in turn impact on an innumerable collection or

set of other co-impacted objects, individuals and events.

• In order to study and prepare for Threat Analysis, Hazard and Risk Identification

and Future Management - we need to bear in mind the fact that all objects and

events are potentially connected in some way or other. None of these random

events occurs in isolation, none are entirely independent or unconnected. Every

object in the Universe exerts an influence over every other object, every process

impacts on every other process – however tenuous these relationships. This

phenomenon of Event Clustering is something that, through our own everyday

experience, we are all familiar with, aware of and know about – and with training

and preparation we are all easily able to follow, analyse, interpret and

understand.


Multiple Random processes also occur in clusters.....

• Random Processes (with the notable exception of Quantum Events) are never truly or

completely random or symmetrical – they are triggered by the manifestation of “unseen

forces” interacting with complex systems. It is the nature of Random Processes to

generate Chaotic Events – which may occur together in multiple, related and similar

sequences as a result of these hidden forces.

• At the local level, we see stochastic processes at work when we experience the myriad

of phenomena that make up our everyday life experiences – which also have a tendency

to occur in groups or clusters. Almost without exception, we hear of events by type

occurring close together in temporal and spatial proximity. The saying that bad or good

news comes in groups has some validity based upon the nature of event clustering.

Human disasters – train, boat or plane accidents, along with natural disasters – volcanic

eruptions, earthquakes and tsunamis – often arrive in groups or clusters aggregated

together in Time and Space – separated by long periods of no such events.


• There is an interesting Sensitive Dependence phenomenon called Phase

Locking where two loosely coupled systems with slightly different frequencies

show a tendency to move into resonance – in order to harmonise with one

another. We also know that the opposite of Attraction (or system convergence)

– Repellence (or system divergence) - is another type of System Dependency

possible with phase-locked systems/ Sensitive Dependence to external

forces demonstrates that minute, imperceptible changes to forces acting during

a system cycle are sufficient to dramatically alter the final state of the system,

which can display diverge trajectories with only very tiny inputs – especially if we

run those harmonised phase-locked systems in reverse.....(why ?)

• Phase locking draws two nearly harmonic systems into resonance and to the

observer, gives us the appearance of a “coincidence”. There are, however, no

such thing as coincidences in Newtonian Physics. Complexity Theory also

shows us that minute, imperceptible changes to input parameters at the initial

state of a system, at the beginning of a cycle - Sensitive Dependence to initial

conditions - are sufficient to dramatically alter the final state after even only a

few iterations of the system cycle. Such “coincidences” are, however, entirely

due to external forces acting upon the system - far beyond our ability to detect.


Attractors and Repellents

• Sensitive Dependence in Complexity Theory tells us that minute, imperceptible

changes to a system – at the beginning of a cycle, or dynamic forces acting as the

cycle evolves - are sufficient to dramatically alter the final state of the system -

even after a relatively few iterations of the system cycle.. Changes to a system at

the initial state constitutes Initial Sensitive Dependence, whilst dynamic external

forces acting on the system as the cycle evolves constitutes Dynamic Sensitive

Dependence. Thus Attractors and Repellents are examples of Dynamic

Sensitive Dependence.

• Any trajectory of the dynamic system in the attractor does not have to satisfy any

special constraints - except for remaining as an attractor. The trajectory may be

periodic or chaotic. In a set of periodic or chaotic points, if the average flow in the

neighbourhood is generally towards the set, then it is an attractor. If the average

neighbourhood flow is generally away from the set, then that set is instead

referred to as a repellent (repellor)..

http://en.wikipedia.org/wiki/Trajectory

http://en.wikipedia.org/wiki/Periodic_function

http://en.wikipedia.org/wiki/Chaos_theory

http://en.wikipedia.org/wiki/Repellor



• An attractor is a set within a dynamic system, towards which a moving variable

evolves over time. That is, points in that set get close enough to the attractor to

remain close - even when slightly disturbed by an external force The evolving

time-variant variable may be represented algebraically as an n-dimensional vector.

• The attractor is a region in n-dimensional space. In physical systems, the n

dimensions may be, for example, three positional coordinates and one temporal

co-ordinate for each of one or more physical entities; in economic systems, they

may be separate variables such as the inflation rate and the unemployment rate.

• If the evolving variable is two- or three-dimensional, the attractor of the dynamic

process can be represented geometrically in two or three dimensions, (as for

example in the three-dimensional case depicted to the right). An attractor can be

a point, a finite set of points, a curve, a manifold, or even a complicated set with

a fractal structure known as a strange attractor. If the variable is a scalar, the

attractor is a subset of the real number line. Describing attractors in dynamic

chaotic systems has been one of the greatest achievements of chaos theory.

http://en.wikipedia.org/wiki/Set_(mathematics)

http://en.wikipedia.org/wiki/Dynamical_system

http://en.wikipedia.org/wiki/Variable_(mathematics)

http://en.wikipedia.org/wiki/Algebra

http://en.wikipedia.org/wiki/Coordinate_vector

http://en.wikipedia.org/wiki/Space_(mathematics)



http://en.wikipedia.org/wiki/Physics

http://en.wikipedia.org/wiki/Economics

http://en.wikipedia.org/wiki/Inflation_rate

http://en.wikipedia.org/wiki/Unemployment_rate

http://en.wikipedia.org/wiki/Geometry

http://en.wikipedia.org/wiki/Point_(geometry)

http://en.wikipedia.org/wiki/Curve

http://en.wikipedia.org/wiki/Manifold

http://en.wikipedia.org/wiki/Fractal

http://en.wikipedia.org/wiki/Attractor

http://en.wikipedia.org/wiki/Scalar_(mathematics)



Strange Attractor

• A Strange Attractor has a fractal dimensional structure. This is often the case when the system

dynamics are chaotic. Strange attractors that are non-chaotic may also exist. The term Strange

Attractor was coined by David Ruelle and Floris Takens to describe the attractor resulting from

a series of bifurcations in a system modelling the heat convection dynamics of a fluid heated

from below and cooled at the top. This process drives Plate Tectonics in the Earth’s mantle –

causing the spreading of Oceans from the mid-oceanic rift and resulting in Continental Drift.

• Strange attractors are often differentiable in a few directions, but some are like Cantor dust, and

are therefore not differentiable. Strange attractors may also be found in presence of noise -

where they may be shown to support invariant Random Probability measures of Sinai-Ruelle-

Bowen type - see Chekroun et al. (2011).

• A Strange Attractor is an attracting set that has zero measure in the embedding phase

space and has fractal dimensions. Trajectories within a strange attractor appear to skip around

randomly. On the surface these three equations seem relatively simple to solve. However, they

represent an extremely complicated and variable dynamic system. If the results are plotted in

three dimensions, then the following three-dimensional figure, called the Lorenz attractor, is

obtained: -



http://en.wikipedia.org/wiki/David_Ruelle


http://en.wikipedia.org/wiki/Floris_Takens



http://en.wikipedia.org/wiki/Bifurcation_theory

http://en.wikipedia.org/wiki/Differentiable

http://en.wikipedia.org/wiki/Homeomorphic

http://en.wikipedia.org/wiki/Cantor_dust

http://mathworld.wolfram.com/Measure.html

http://mathworld.wolfram.com/PhaseSpace.html


http://mathworld.wolfram.com/Fractal.html


• There is a further interesting phenomenon called Phase Locking where two

loosely coupled systems with slightly different frequencies show a tendency to

move into resonance – they are seeking to harmonise with one another. We also

know that the opposite of system convergence - system divergence - is also

possible with phase-locked systems, Sensitive dependence also tells us that very

tiny inputs are enough to completely alter the final state after several iterations of

the dynamic. We thus know of systems that diverge with only very tiny inputs, but

the opposite is also true with convergence, especially if we run things in reverse.

• Thus phase locking draws two nearly harmonic systems into resonance and gives

us the appearance of a “coincidence”. There are, however, no coincidences in

nature or Physics - all random processes (with the notable exception of Quantum

Events) – are neither truly random nor completely symmetrical – but are simply the

outcome of unseen forces acting on a system. Such 'coincidences' are like the

clusters of personality types that are governed by certain recurring planets -

according to the statistical researches of M. Guaquelin.

http://www.knowledge.co.uk/frontiers/sf061/sf061b06.htm



• Phase Divergence drives two phase-locked harmonic systems out of

synchronisation into random, chaotic and discordant behaviour - where phase

locked systems can diverge from each other with only very tiny inputs (especially

when we run those phase-locked harmonic system models in reverse).....

• It is safe to say that pure coincidence is a vanishingly small reality. In fact, it is safe to

say that phenomena such as coincidence (which is more properly called serendipity)

The fact that very complex systems are invoked – as seen drawing two interacting

bodies into perfect resonance - is due to unknown factors or unseen forces behind

effects such as phase locking, and sensitive dependence. Sensitive dependence and

the interaction of every object upon all of the rest accounts for the phenomenon of

clustering – not serendipity, coincidence or mere chance.....

• The structure of the universe is based on such stochastic events. Here too, we find

random clustering events. The distribution of matter in the universe is based on the

quantum foundation. Clustering at the quantum level when the universe was just a

few thousands of a millimetre across – has lead to the creation of the super massive

black holes at the centre of each galaxy which, through gravitational attraction drive

the clustering of star / planetary systems, star clusters, galaxies and galactic clusters.

http://www.umbrars.com/sr/introsr.htm


• There are many kinds of stochastic or random processes that impact on every area of Natural

Cycles and Human Activity. Randomness can be found in Science and Technology and in

Humanities and the Arts. Random events are taking place almost everywhere we look – for

example from Complex Systems and Chaos Theory to Cosmology and the distribution and

flow of energy and matter in the Universe, from Brownian motion and quantum theory to

Fractal Branching and linear transformations. Further examples include Random Events,

Weak Signals and Wild Cards occurring in each aspect of Nature and Human Activity – from

Ecology and the Environment to Weather Systems and Climatology in Economics and in the

Biological basis of Behaviour. And then there are the examples of atmospheric turbulence,

and the complex orbital and solar interaction cycles – and much, much more besides.....

• There is an interesting phenomenon called Phase Locking where two loosely coupled

systems with slightly different frequencies show a tendency to move into resonance – in order

to harmonise with one another. We also know that the opposite of system convergence -

system divergence - is also possible with phase-locked systems, which can also diverge with

only very tiny inputs - especially if we run those systems in reverse. Thus phase locking

draws two nearly harmonic systems into resonance and gives us the appearance of a

“coincidence”. There are, however, no coincidences in Physics. Sensitive Dependence in

Complexity Theory also tells us that minute, imperceptible changes to inputs at the initial state

of a system, at the beginning of a cycle, are sufficient to dramatically alter the final state after

even only a few iterations of the system cycle.

Random Event Clustering – Patterns in the Chaos.....

Order out of Chaos – Patterns in the Randomness

• The long horizon of predictability for astronomical cycles and planetary alignment allows us to determine when events associated with the movement of the planets will exhibit a trendy to cluster. Planetary clustering in a non-cyclic periodic fashion will generate non-cyclic periodic effects, each object (sun, moon, planets) impacting upon all others.

• The Earth is not exempt from the forces of these objects. They manifest in many ways, obvious and subtle. Some are easy to understand, others are not. We can calculate the perturbation and tidal influences with some ease and match these with the real-life nature of these influences and effects that we experience. The Psychic influences of stochastic clustering are much harder to track. Such “coincidences” are the clusters of personality types that are “governed” by certain planetary influences - according to the statistical research of M. Guaquelin.

• We can calculate the perturbation and tidal influences with some ease and match these with real effects we experience. The psychic influences are much harder to track. They are there nonetheless as evidenced by the lunar and solar influences. The stochastic and clustering nature of these influences is what is behind the seeming stochastic and clustering nature of events we experience.





• Even when we look to the formation of solar systems, we see stellar evolution

mediated by forces, random events and harmonics in synchronicity. As random

events tend to cluster as part of the natural evolution of the universe, it is not

surprising to find that, as a natural consequence of this clustering, all complex

systems will evolve in a similar way. Planets in orbit around a star must have

orbital periods in dissonance to each other in order to have reasonable stability.

• This dissonance will evolve to create patterns that occur randomly in space and

time where planets aggregate together along one line of sight or another. Such

is the nature of the great planetary conjunctions - stelliums. In our solar system,

this kind of planetary alignment occurs roughly once very forty years, but no two

stelliums are ever exactly alike in planetary grouping, distribution or location in

reference to the other objects in the solar system - or even in alignment with

background stellar objects. Since planets orbit in more or less well-defined and

periods, these events are highly predictable - unlike the events in the quantum

realm or with a chained sequence of coin tosses forming random event clusters.


• Every Risk Analyst, Contingency Planner or Disruptive Futurist is continuously seeking to discover a combination, sequence or chain of events which occur together in clusters – and when acting together demonstrate either transient or instantiated dependencies (are interacting or co-related) or both. That is, they are impacting with each other in some way or another in a manner whereby we are able to forecast the next event. Basically, we need to apply complex systems and chaos theory thinking to resolving all Future Domain problems, opportunities, threats, issues or challenges.

• What factors or forces do we need to consider as being in-scope and critical to the behaviour of the system? Which other factors or forces have we ignored, overlooked or not considered? What further unknown factors or unseen forces are there which we have not detected – but may still exist – and what unknown factors or unseen forces are somehow exerting influence over the behaviour of the system subject to the study - thus influencing the behaviour of that systems elements as it evolves through Space and Time?

Quantitative (Technical) Analysis

• Quantitative (Technical) Analysis involves studying detailed micro-economic models which process vast quantities of Market Data (commodity price data sets). This method utilises a form of historic data analysis technique which smoothes or profiles market trends into more predictable short-term price curves - which will vary over time within a specific market.

• Quantitative (Technical) Analysts can initiate specific market responses when prices reach support and resistance levels – via manual information feeds to human Traders or by tripping buying or selling triggers where autonomous Computer Trading is deployed. Technical Analysis is data-driven (experiential), not model-driven (empirical) because our current economic models do not support the observed market data. The key to both approaches, however, is in identifying, analysing, and anticipating subtle changes in the average direction of movement for Price Curves – which in turn reflect relatively short-term Market Trends.

Quantitative v. Qualitative Domains Quantitative (Technical)

Qualitative (Narrative)

Futures Studies

Numeric Definitive

Quantitative

(Technical) Analysis

Investigative

Descriptive

Analytic

Social Sciences

Sociology

Economics





Life Sciences




Biology Ecology

Climate Change

“Goal-seeking” Empirical Research Domains Formulaic


Earth Sciences


Applied mathematics

Future Management

Environmental Sciences



Weather Forecasting

Particle Physics

String Theory

Statistics

Strategic Foresight







Astronomy

Cosmology

Relativity

Astrophysics

Quantitative Analysis Pure mathematics

Geography

Geology

Archaeology

Economic Analysis


Chemistry

Engineering

Astrology

Geo-physics


Palaeontology

Cluster Theory

Interpretive

Qualitative

(Narrative) Analysis

Quantum Mechanics


Quantitative / Qualitative Analysis Techniques

TECHNICAL (QUANTITATIVE) METHODS TECHNICAL (QUANTITATIVE) METHODS (cont.)

Asymptotic Methods and Perturbation Theory Statistical Arbitrage

“Big Data” - Statistical analysis of very large scale (VLS) datasets Technical (Quant) Analysis

Capital Adequacy – Liquidity Risk Modelling – Basle / Solvency II Trading Strategies - neutral, HFT, pairs, macro; derivatives;

Convex analysis Trade Risk Modelling: – Risk = Market Sentiment – Actual Results

Credit Risk Modelling (PD, LGD) Value-at-Risk (VaR)

Data Audit, Data Profiling. Data Mining and CHAID Analysis Volatility modelling (ARMA, GARCH)

Derivatives (vanilla and exotics)

Dynamic systems behaviour and bifurcation theory NARRATIVE (QUALITATIVE) METHODS

Dynamic systems complexity mapping and network reduction

Differential equations (stochastic, parabolic) “Big Data” -, Clinical Trials ,Morbidity and Actuarial Outcomes

Extreme value theory Business Strategy, Planning, Forecasting Simulation and Consolidation

Economic Growth / Recession Patterns (Boom / Bust Cycles) Causal Layer Analysis (CLA)

Economic Planning and Long-range Forecasting Chaos Theory

Economic Wave and Business Cycle Analysis Cluster Theory

Financial econometrics (economic factors and macro models) Complexity Theory

Financial time series analysis Complex (non-linear) Systems

Game Theory and Lanchester Theory Complex Adaptive Systems (CAS)

Integral equations Computational Theory (Turing)

Interest rates derivatives Delphi Oracle /Expert Panel / Social Media Survey

Ordered (Linear) Systems (simple linear multi-factor equations) Economic Wave Theory – Business Cycles (Austrian School)

Market Risk Modelling (Greeks; VaR) Fisher-Pry Analysis and Gomperttz Analysis

Markov Processes Forensic “Big Data” – Social Mapping and Fraud Detection

Monte Carlo Simulations and Cluster Analysis Geo-demographic Profiling and Cluster Analysis

Non-linear (quadratic) equations Horizon Scanning, Monitoring and Tracking

Neural networks, Machine Learning and Computerised Trading Information Theory (Shannon)

Numerical analysis & computational methods Monetary Theory – Money Supply (Neo-liberal and Neo-classical)

Optimal Goal-seeking, System Control and Optimisation Pattern, Cycle and Trend Analysis

Options pricing (Black-Scholes; binomial tree; extensions) Scenario Planning and Impact Analysis

Price Curves – Support / Resistance Price Levels - micro models Social Media – market sentiment forecasting and analysis

Quantitative (Technical) Analysis Value Chain Analysis – Wealth Creation and Consumption

Statistical Analysis and Graph Theory Weak Signals, Wild Cards and Black Swan Event Forecasting

Qualitative (Narrative) Analysis

• Qualitative (Narrative) Analysis involves further processing of summarised results generated by Quantitative (Technical) Analysis - super sets of many individual micro-economic model runs. Techniques such as Monte Carlo Simulation cycle macro-economic model runs repeatedly through thousands of iterations – minutely varying the starting conditions for each and every individual run cycle.

• Results appear as a scatter diagram consisting of thousands of individual points for commodity prices over a given time line. Instead of a random distribution – we discover clusters of closely related results in a background of a few scattered outliers. Each of these clusters represents a Scenario – which is analysed using Cluster Analysis methods - Causal Layer Analysis (CLA), Scenario Planning and Impact Analysis– where numeric results are explained as a narrative story about a possible future outcome – along with the probability of that scenario materialising.


• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave

frequency analysis in Physics – and is based on Time-frequency analysis – a

technique which exploits the wave frequency and time symmetry principle. This is

introduced here for the first time in the study of human activity waves, and in the

field of economic cycles business cycles, patterns and trends.














in nature to biological behaviour in an organism than to Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable



shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards).

Unexpected and surprising Cycle Pattern changes have historically occurred

during regional and global conflicts being fuelled by technology innovation-driven

arms races - and also during US Republican administrations (Reagan and Bush -

why?). Just as advances in electron microscopy have revolutionised biology -

non-stationary time series wave-form analysis has opened up a new space for


The Temporal Wave

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.


(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

Composite Economic Wave Series








Weak Signals, Wild Cards and

Black Swan Event Scenarios • In this section, we examine empiric evidence from global “Big Data” on how shock waves

to geo-political economic and business systems impact on business cycles, patterns and

trends. We first review Gail's work (1999), which uses long-running restrictions to identify

shock waves, and examine whether the identified shocks can be plausibly interpreted: -

• Wild card and Black Swan Event Types

– Technology Shock Waves

– Supply / Demand Shock Waves

– Political, Economic and Social Change

– Global Conflict – War, Terrorism, and Insecurity

– Natural Disasters and Catastrophes – Global Massive Change Events

• We do this in three ways. Firstly, we derive additional long-run restrictions and use them as

identification tests. Secondly, we compare the qualitative implications from the model with the

impulse responses of variables such as production, wages and consumption. Third, we test

whether some standard .exogenous. variables predicate the shock events. We discovered that

that Weak Signals may predicate coming technology shock waves, oil price shocks, and military

conflict. We then show ways in which a standard DGE model can be modified to fit Gail's finding

that a positive technology shock may lead to lower labour input. Finally, we re-examine the

properties of the other key shocks to the economic system and demonstrate the impact of oil

price shocks and military conflict .

Price Index Inflation

Waves, Cycles, Patterns and Trends

• Business Cycles were once thought to be an economic phenomenon due to periodic fluctuations in economic activity. These mid-term economic cycle fluctuations are usually measured using Real (Austrian) Gross Domestic Product (rGDP). Business Cycles take place against a long-term background trend in Economic Output – growth, stagnation or recession – which affects Money Supply as well as the relative availability and consumption (Demand v. Supply and Value v. Price) of other Economic Commodities. Any excess of Money Supply may lead to an economic expansion or “boom”, conversely shortage of Money Supply (Money Supply shocks – the Liquidity Trap) may lead to economic contraction or “bust”. Business Cycles are recurring, fluctuating levels of economic activity experiences in an economy over a significant timeline (decades or centuries).

• The five stages of Business Cycles are growth (expansion), peak, recession (contraction), trough and recovery. Business Cycles were once widely thought to be extremely regular, with predictable durations, but today’s Global Market Business Cycles are now thought to be unstable and appear to behave in irregular, random and even chaotic patterns – varying in frequency, range, magnitude and duration. Many leading economists now also suspect that Business Cycles may be influenced by fiscal policy as much as market phenomena - even that Global Economic “Wild Card” and “Black Swan” events are actually triggered by Economic Planners in Government Treasury Departments and in Central Banks as a result of manipulating the Money Supply under the interventionist Fiscal Policies adopted by some Western Nations.

Randomness


Classical Mechanics

(Newtonian Physics)



Sir Isaac Newton


Watt, Stephenson









Werner Heisenberg,

Paul Dirac,

Richard Feynman

Randomness









Celestial Objects









Celestial Objects

Hoyle, Ryall, Rees,






Albert Einstein,

Hermann Minkowski,

Stephen Hawking

Wave Mechanics

(String Theory or

Quantum Dynamics)

The Universe,

Membranes and

Hyperspace

Michael Green,

Michio Kaku


• Randomness makes precise prediction of future outcomes impossible. We are unable to predict any

outcome with any significant degree of confidence or accuracy – due to the inherent presence of

randomness and uncertainty associated with Complex Systems. Randomness in Complex Systems

introduces chaos and disorder – causing disruption. Events no longer continue along a predictable linear

course leading towards an inevitable outcome – instead, we experience surprises.

• What we can do, however, is to identify the degree of uncertainty present in those Systems, based on

known, objective measures of System Order and Complexity - the number and nature of elements

present in the system, and the number and nature of relationships which exist between those System

elements. This in turn enables us to describe the risk associated with possible, probable and alternative

Scenarios, and thus equips us to be able to forecast risk and the probability of each of those future

Scenarios materialising.

• If true randomness exists and future outcomes cannot be predicted – then what is the origin of

that randomness? For example, are unexpected outcomes simply apparent as a result of sub-

atomic nano-randomness existing at the quantum level – such as uncertainty phenomena etc…..?

• The Stephen Hawking Paradox postulates that uncertainty dominates complex and chaotic systems to

such an extent that future outcomes are both unknown - and unknowable. The working context of this

paradox is restricted, however, to the realm of Quantum Mechanics – where each and every natural event

that occurs at the sub-atomic level is truly intrinsically and completely both symmetrical and random.


• What is the explanation for randomness evident in all high-order phenomena

found in nature…?



limited Systemic Models which attempt to simplify, abstract and simulate reality -

must be clearly distinguished between and understood.

• When we design our Systemic Models representing Actual Reality – such as the

Economy, Geo-political systems, Climate Change, Weather and so on - if we are

lucky enough, then some high-order phenomena found in nature may be captured

by a random rule; and with even more luck, by a deterministic rule (which can be

regarded as a special case of randomness) - but if we are unlucky - then those

rules might not be no captured at all. Regarding the nature of reality - it still

remains unclear what factors distinguish truly random phenomenon found in

nature at the Quantum level (e.g. radioactive decay?) from Random Events which

are triggered by unseen forces.


• Can we accept that these natural phenomena are not truly random at all – that is, given sufficient

information such as complete event data sets - it is possible to predict random events? If so,

are all random events the result of the same natural phenomenon - unseen or hidden forces ? “

• Classical (Newtonian) Physics describe the laws which govern all of the systems and objects that we are

familiar with in our everyday routine lives. Relativity Theory, on the other hand, describes unimaginably

large things, whilst Quantum Mechanics describes impossibly small things – and Wave Theory (String

Theory) attempts to describe everything. True randomness does not really exist in Classical (Newtonian)

Physics – the laws which control Chaos and Complex Systems that govern every aspect of our life on Earth

today – from Natural Systems such as Cosmology, Astronomy, Climatology, Geology and Biology through to

Human Activity Systems such as Political, Economic and Sociological Complex Adaptive Systems (CAS).

Randomness is simply the results of those forces which are not known, not recognised, not understood, are

not under the control of the observer or simply occur outside of the known boundaries of observable system

components – but, nevertheless, must still exist and exert influence over the system. Over many System

Cycles, immeasurably small inputs interacting with Complex System components and relationships - may

be amplified into extremely significant outputs.....

1. Classical (Newtonian) Physics – apparent randomness is as a result of Unknown Forces

2. Relativity Theory – any apparent randomness or asymmetry is as a result of Quantum effects

3. Quantum Mechanics – all events are truly and intrinsically both symmetrical and random

4. Wave (String) Theory – apparent randomness and asymmetry is as a result of Unknown Forces -

which may in turn have their origination in Quantum Mechanics effects


Weak Signals, Wild Cards and Black Swan Events









• Randomness may be somewhat difficult to demonstrate, as Randomness in chaotic system behaviour is not always readily or easily distinguishable from any other “noise” that we may find in Complex Systems – such as foreground and background wave harmonics, resonance and interference patterns. Complex Systems may be influenced by both internal and external factors which remain hidden - unrecognised or unknown. These unknown and hidden factors may lie far beyond our ability to detect them. The existence of weak internal or external forces simply may not be visible to the observer – the subliminal temporal forces which nevertheless can influence Complex System behaviour in such a way that the presence of imperceptibly tiny inputs, propagated and amplified over many system cycles - are able to create massive observable changes to outcomes in complex system behaviourr.

• Randomness. Neither data-driven nor model-driven macro-economic or micro-economic models currently available to us today - seem able to deal with the concept or impact of Random Events (uncertainty). We therefore need to consider and factor in further novel and disruptive (systemic) approaches which offer us the possibility to manage uncertainty. We can do this by searching for, detecting and identifying Weak Signals – small, unexpected variations or disturbances in System outputs indicating hidden data within the general background System “noise” - which in turn may predicate the possible future existence or presence of emerging chaotic, and radically disruptive Wild Card or Black Swan events beginning to form on the detectable Horizon – or even just beyond. Random Events can then be factored into Complex Systems Modelling. Complex Systems interact with unseen forces – which in turn act to inject disorder, randomness, uncertainty, chaos and disruption. The Global Economy, and other Complex Adaptive Systems, may in future be considered and modelled successfully as a very large set of multiple interacting Ordered (Constrained) Complex Systems - each individual System loosely coupled with all of the others, and every System with its own clear set of rules and an ordered (restricted) number of elements and classes, relationships and types.


• Weak Signals, Strong Signals, Wild Cards and Black Swan Events – are a sequence of waves linked and integrated in ascending order of magnitude, which have a common source or origin - either a single Random Event instance or arising from a linked series of chaotic and disruptive Random Events - an Event Storm. These Random Events propagate through the space-time continuum as a related and integrated series of waves with an ascending order of magnitude and impact – the first wave to arrive is the fastest travelling,- Weak Signals - something like a faint echo of a Random Event which may in turn be followed in turn by a ripple (Strong Signals) then possibly by a wave (Wild Card) - which may indicate the unfolding a further increase in magnitude and intensity which finally arrives catastrophically - something like a tsunami (Black Swan Event).


1. Random Event 1. Discovery

2. Weak Signals 1.1 Establishment

3. Strong Signals 1.2 Development

4. Wild Cards 2. Growth

5. Black Swan Event 3. Plateau

4. Decline

5. Collapse

5.1 Renewal

5.2 Replacement

Weak Signals

• Weak Signals – are subliminal temporal indicators of ideas, patterns or trends

coming to meet us from the future – or perhaps indicators of novel and

emerging, ideas, influences and messages which may interact with both

current and pre-existing patterns and trends to impact or affect some change

taking place in our current environment – or even an early warning or news of

impending random events or catastrophes which, at some point in the Future,

time or place, may predicate, influence or impact on future processes or

events - or effect subtle to major changes in how we live, work and play.

• A Weak Signal is an early warning of change, which typically becomes

stronger by combining with other signals. The significance of a weak future

signal is determined by the objectives of its recipient, and finding it typically

requires systematic searching. A weak future signal requires: i) support, ii)

critical mass, iii) growth of its influence space, and dedicated actors, i.e. ‘the

champions’, in order to become a strong future signal, or to prevent itself from

becoming a strong negative signal. A Weak Future Signal is often recognised

by pioneers or special groups - not by acknowledged subject matter experts.

Weak Signals Weak Signal Property Different views and viewpoints

1 Nature Weak Signals are subtle indicators of ideas, patterns or trends that give us a glimpse into the future – predicating possible future transformations and changes which are happening on or even just over the visible horizon, changes in how we do business, what business we do, and the future environment in which we will all live and work.

2 Quality

Weak Signals may be novel and surprising from the signal analyst's vantage point - although many other signal analyst's may have already, failed to recognise, misinterpreted or dismissed the same Weak Signals

3 Purpose Weak Signals are used for Horizon Scanning, Tracking and Monitoring and for Future Analysis and Management

4 Source Weak Signals, Strong Signals, Wild Cards and Black Swan Events – are a sequence of waves linked and integrated in ascending order of magnitude, which have a common source or origin - either a single Random Event instance or arising from a linked series of chaotic and disruptive Random Events – crearing a Random Event Cluster or Random Event Storm.


5 Wave-form Analytics and “Big Data” Global Internet Content

Wave-form Analytics may be used with “Big Data” to analyse how Random Events propagate through the space-time continuum in a related and integrated series of waves with an ascending order of magnitude and impact – the first wave to arrive is the fastest travelling - Weak Signals - something like a faint echo of a Random Event which may be followed in turn by a ripple (Strong Signals) then possibly by a wave (Wild Card) - which may indicate the unfolding a further increase in magnitude and intensity which finally arrives catastrophically - something like a tsunami (Black Swan Event).

6 Identification Weak Signals are sometimes difficult to track down, receive, tune in, identify, amplify and analyse amid the overwhelming volume of “white noise” from stronger signals and other foreground and background noise sources

7 Principle of Dual Nature (possibility of either an Opportunity or Threat)

Weak Signals may indicate the possibility of either a potential future threat or opportunity to yourself or your organization - or foretell the pending arrival of a future advantage or reversal – a “Wild card” or Black Swan” event


8 Perception Weak Signals are often missed, dismissed or scoffed at by other Subject Matter Experts

9 Opportunity Weak Signals contain novel and emerging ideas, influences and messages - therefore they represent an early window of potential opportunity.

10 Impact Weak Signals arrive, become established, develop, grow and mature - then peak, plateau decline and collapse – or interact with current and pre-existing extrapolations, patterns or trends to transform or change the landscape

11 Receipt / Observation Every Weak Future Signal requires – 1. a Receiver / Observer / Analyst (which could be

automated by deploying “Big Data” Analytics) 2. Subject mater experts, special interest groups etc. and

Empowered Stakeholders to achieve critical momentum 3. growth of its support, championship and influence space 4. dedicated actors, e.g. “supporters and champions”

Weak Signals

Weak Signal Property Different views and viewpoints

12 Duration Weak signals only last for a brief period: – Transient Signal 1. Weak signals are seen as a sign that lasts for a moment,

but a phenomenon behind it lasts longer – a following Strong Signal

2. Weak signals are phenomena that last for a short Period of time (wild cards?)

Weak signal lasts longer:– it now becomes a Strong Signal 3. A weak signal is a cause for a change in the future 4. A weak signal is itself a change phenomenon

13 Transition phenomenon 1. A weak signal phenomenon is a result of a Random Event 2. A weak signal is a sign of a future disruptive changes or

Individual / Local / Regional / Global Transformations 3. A weak signal may be a member of a larger Wave Series 4. The transition phenomenon of a weak signal is that in the

future it will either get stronger (becomes a Strong Signal) or weaker (attenuate and disappear from view)

Weak Signals


14 Objectivity v. Subjectivity 1. Weak signals exist independently of their receiver. 2. “Weak signals float in the phenomena space and

wait for someone to find them” – automation via “Big Data” Analytics can address this issue.....

3. A weak signal does not exist without reception / interpretation by a receiver / observer (which may mitigated by automated via “Big Data” Analytics)

15 Interpretation The interpretation of a same signal can be different from the viewpoint of different receivers of the signal. Human Interpretation adds subjectivity to the signal – even though it is thought to be objective – “Big Data” Analytics may be used for the Validation process

16 Signal Strength over Time 1. The weak signal (as an indicator) is strengthening 2. A phenomenon, interpreted as weak signal, is

strengthening – it now becomes a Strong Signal 3. A phenomenon whose status is in question, is

strengthening – it now becomes a Strong Signal

Weak Signals


17 Roles and Responsibilities – Receivers /Observers / Analysts of the weak signal (who receives, identifies, observes and classifies)

1. Difficulties in defining the concept of Weak Signals arising from a single instance or linked series of Random Events – or from an Event Cluster or Storm – to Empowered Stakeholders, subject mater experts, special interest groups, etc.

2. Differences in opinion on signal content between signal Receiver, Observer and Analysts :- resolved by special interest groups, subject mater experts

18 Roles and Responsibilities – Analysts / Interpreters / Stakeholders in the signal (who analyses and draws useful valid conclusions)

1. Who is drawing the conclusions on the cause-effect relationship? – the Receiver and the Observer

2. Who is defining the credibility and significance of weak signal? – the Observer and the Analyst

3. Who is the one that can affect important decisions concerning the future? – Empowered Stakeholders

Strong Signals

• Strong Signals – represent the first clear and visible presence of a Random Event – the

secondary arrival of stronger but slower-travelling waves containing more information of

possible, probable and alternative future events – random events, future catastrophes, or

indications o novel and emerging, ideas, influences and messages which may interact with

both current and pre-existing patterns and trends to impact or affect some change taking

place in our environment - at some point, time or place in the future – for example, what

future climatic and ecological environment will live , work and play in what political, social

and economic environment will live , work and play in, how we live, work and play, what

business we do, how we do business and who we do Business with......

1. Strong Signals may demonstrate a substantial lag time before they follow their

preceding indicators, prior Weak Signals

2. Strong Signals may contain confirmation about future events – random events,

catastrophes, or indications o novel and emerging, ideas, influences and messages.

They therefore present a second potential window of opportunity if the first Weak Signals

in the series were undetected, overlooked or dismissed

3. Strong Signals arrive, become established, develop, grow and mature - then peak,

plateau decline and collapse or interact with current and pre-existing extrapolations,

patterns or trends which act to transform or change the current outlook or landscape.

Wild Cards

• Wild Cards – are any sudden and unexpected Local or Regional variation or change in the status or

condition of natural (environmental, ecological) or Human (military, political, social or economic)

perspectives - which promises either a new, unexpected or significant advantage, gain or opportunity -

or the prospect of the loss or failure of an important asset, capability or facility – and may also contain

within itself the threat of a possible future advantage or reversal – a “Wild card” or Black Swan” event.

1. Wild Card Events have been defined, for example, by Rockfellow (1994), who speculated that a wild card is "an

event having a low probability of occurrence, but an inordinately high impact if it does occur."

2. Wild Cards may represent either a potential threat or perceived opportunity to yourself and / or your organization -

and may contain within them, the seeds of a possible major future global advantage or reversal – a forthcoming

“Black Swan” event

3. Listing examples of specific 21st Century Wild Cards in 1994, Rockfellow defined three wild cards principles: -

1. 21st Century Wild Cards manifest at the beginning of the Business Cycle (i.e. within 11 years of a prior

cycle),

2. 21st Century Wild Cards have a probability of occurring at a rate of less than 1 in 10 years,

3. 21st Century Wild Cards events will likely have high impact on international businesses

4. Wild Cards are "low-probability, hi-impact events that happen quickly" and "they have huge sweeping

consequences." Wild cards, according to Petersen, generally surprise everyone, because they materialize so

quickly that the underlying social systems cannot effectively anticipate or respond to them (Petersen 1999).

5. According to Cornish (2003: 19), a Wild Card is an unexpected, surprising or even startling event that has sudden

impact, important outcomes and far-reaching consequences. He continues: "Wild cards have the power to radically

change many processes and events and to entirely overturn people's thinking, planning and actions."

Black Swan Events

• Black Swan Events – refer to any unforeseen, sudden and extreme Global-level

transformation or change events in either the military, political, social, economic or

environmental landscape - having an inordinately low probability of occurrence -

coupled with an extraordinarily high impact when they do occur (Nassim Taleb).

1. Black Swan Events are a complete and totally unexpected surprise to the observer

2. Black Swan Events have a major impact as a catalyst or agent of global

transformation and change.

3. Black Swan Events may represent wither a potential catastrophic threat or novel,

unexpected opportunities.

4. At its first appearance, the Black Swan Event is rationalized by hindsight, as if it

could or should have been expected (e.g., the relevant Weak Signals were available

but not detected, identified or accounted for).

Randomness

Black Swan – Nassim Taleb

• Black Swan by Nassim Taleb was first published in 2007 and quickly sold out, with close

to 3 million copies purchased as of February 2011. Fooled by Randomness and Black

Swan seized the public imagination, and quickly generated mass-market interest to create

a new, niche market segment for Future Management publications - which cross-over

General Interest, Professional and Academic sectors. Taleb's non-technical writing style

mixes a narrative text (often semi-autobiographical) and whimsical home-spun tales

backed up by some historical and scientific content. The success of Taleb's first two books

(Fooled by Randomness and the Black Swan) gained him an advance on Royalties of

$4 million for his follow-up book – the Blank Swan.

The Drunkard's Walk:- How Randomness Rules Our Lives - Leonard Mlodinow

• The Drunkard's Walk dives deeper into Randomness. This book is different - it is natural

for scientific books to discuss science – but unusual for them to contain highly readable

prose and good humour, not to mention useful and practical insights which help to live your

life with a greater understanding of the world about you. The book's major weakness is

that it comes up short on fundamental explanations of Chaos, Disruption, Complexity and

Randomness. Mlodinow simply advises readers to "be aware" and "conscious" of how

important randomness is.

Temporal Disturbances in the Space-Time Continuum – Random Events

• Randomness. Weak Signals, Wild Cards and Black Swan Events – may be evidence of radically

disruptive and chaotic Random Events which propagate through the Space-Time Continuum in the

same way as a ripple or wave crosses the ocean. Random Events may be able to “bend” the Space-

Time continuum- which brings two discrete points on different Hyperspace Planes closer together

over a time interval extended through a time-line along the Time axis of the Minkowski Space-Time

Continuum. Perhaps some Wave Types - Weak Signals, Wild Cards and Black Swan Events - can

travel faster or take a different route – perhaps because their Wave forms can propagate through

Space-Time more rapidly than the Wave forms of the other types of Wave - or specific Wave Types

are able to take a “short cut” between two points on different Hyperspace Planes.

• • CASE STUDY • A Pyroclastic Volcanic Eruption begins with a series of linked and integrated

events which have a common origin or source - which in turn generate a sequence of waves in

ascending orders of magnitude. Pyroclastic Volcanic Eruptions begin with a sequence of Random

Events - in this case, it is a sequence of chaotic and disruptive Earthquakes somewhere deep under

an Mountain Chain which is built up from Andesitic Volcanoes – such as the Andes Mountain Chain.

The Andes Mountains are located near an Oceanic Plate subduction zone – an area where the Pacific

Oceanic Plate plunges under the South American Continental Margin. Sediment, sea water and

organic remains from the Ocean floor are carried down towards earth’s mantle and heat up as the

Oceanic Plate plunges deeper into the Earth’s Mantle. Liquids and gases released by this heating

cause the rocks in the Earth’s Mantle to melt, turning from a plastic semi-solid into a liquid. This liquid

then rises through the Earth’s crust and travels towards the surface, collecting in pools forming

Magma Chambers - before finally breaking at the surface through and erupting as Volcanic Magma


• Randomness. Weak Signals, Wild Cards and Black Swan Events – may be evidence of radically disruptive

and chaotic Random Events which propagate through the Space-Time Continuum in the same way as a ripple or

wave crosses the ocean. Random Events may be able to “bend” the Space-Time continuum- which brings two

discrete points on different Hyperspace Planes closer together over a time interval extended through a time-line

along the Time axis of the Minkowski Space-Time Continuum. Perhaps some Wave Types - Weak Signals, Wild

Cards and Black Swan Events - can travel faster or take a different route – perhaps because their Wave forms

can propagate through Space-Time more rapidly than the Wave forms of the other types of Wave - or specific

Wave Types are able to take a “short cut” between two points on different Hyperspace Planes.

• • CASE STUDY • A Pyroclastic Volcanic Eruption begins with a series of linked and integrated events which

have a common origin or source - which in turn generate a sequence of waves in ascending orders of magnitude.

Pyroclastic Volcanic Eruptions begin with a sequence of Random Events - in this case, it is a sequence of

chaotic and disruptive Earthquakes somewhere deep under an Mountain Chain which is built up from Andesitic

Volcanoes – such as the Andes Mountain Chain. The Andes Mountains are located near an Oceanic Plate

subduction zone – an area where the Pacific Oceanic Plate plunges under the South American Continental

Margin. Sediment, sea water and organic remains from the Ocean floor are carried down towards earth’s mantle

and heat up as the Oceanic Plate plunges deeper into the Earth’s Mantle. Liquids and gases released by this

heating cause the rocks in the Earth’s Mantle to melt, turning from a plastic semi-solid into a liquid. This liquid

then rises through the Earth’s crust and travels towards the surface, collecting in pools forming Magma Chambers

- before finally breaking at the surface through and erupting as Volcanic Magma

• Earthquakes are created when the Continental and Oceanic Plates stick together – and periodically unzip and

slide over each other - causing a sequence of tremors or waves. P-waves oscillate up-and-down, whilst S-waves

oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly through the earth in a

related and integrated series of waves - but travelling at different speeds. The first waves to arrive at an observer

of the event are vertical (up / down) disturbances (P-waves) which are followed moments later by a horizontal

(side-to-side) disturbance (S-waves) which have increased magnitude and intensity.


• Tectonic Earthquakes are created when Continental and Oceanic Plates stick together –

then periodically unzip and slide over each other – thus creating a sequence of tremors or

waves. P-waves oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P

and S waves from the Earthquake propagate rapidly through the earth in a related and

integrated series of waves - but travelling at different speeds. The first waves to arrive at

an observer of the event are vertical (up / down) disturbances (P-waves) which are

followed moments later by a horizontal (side-to-side) disturbance (S-waves) which have

increased magnitude and intensity.

• P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster

P-waves are followed by slower but more dramatic and intense S-waves – these Strong

Signals are now giving indications of what is about to follow. Next in the sequence is the

Wild Card Event. As the volcano erupts, its ash cloud billows high into the atmosphere.

Finally the Black Swan Event arrives. As the volcano continues to erupt, the ash

column can no longer support its own weight. It collapses in onto itself and plunges down

the slopes of the Volcano. Surging relentlessly downhill, the catastrophic disturbance of

the Pyroclastic wave destroys all life and covers the landscape with layers of suffocating,

burning hot ash, a black wave covering over everything that lies before it. This, for

example, is what happened when Vesuvius erupted and covered Herculaneum and

Pompeii with over twenty metres of superheated magma erupting as gases and ash.

•


• Earthquakes are created when the Continental and Oceanic Plates stick together – and periodically unzip and slide over each other - causing a sequence of tremors or waves. P-waves oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of waves - but travelling at different speeds. The first waves to arrive at an observer of the event are vertical (up / down) disturbances (P-waves) which are followed moments later by a horizontal (side-to-side) disturbance (S-waves) which have increased magnitude and intensity.

•

• P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster P-waves are followed by slower but more dramatic and intense S-waves – these Strong Signals are now giving indications of what is about to follow. Next in the sequence is the Wild Card Event. As the volcano erupts, its ash cloud billows high into the atmosphere. Finally the Black Swan Event arrives. As the volcano continues to erupt, the ash column can no longer support its own weight. It collapses in onto itself and plunges down the slopes of the Volcano. Surging relentlessly downhill, the catastrophic disturbance of the Pyroclastic wave destroys all life and covers the landscape with layers of suffocating, burning hot ash, a black wave covering over everything that lies before it. This, for example, is what happened when Vesuvius erupted and covered Herculaneum and Pompeii with over twenty metres of superheated magma erupting as gases and ash.

•

• • CASE STUDY • A Tsunami Event consists of a sequence of linked and integrated waves in ascending orders of magnitude which have a common origin or source – in this case, the Random Events begin with a series of chaotic and disruptive Earthquakes somewhere offshore in a subduction zone at a Continental and Oceanic Plate Margin. Earthquakes are formed as the Continental and Oceanic Plates stick together – and then unzip, causing a sequence of random and chaotic tremors. The P and S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of waves travelling at different speeds – the first to arrive are vertical (up / down) disturbances (P-waves) which are followed by a horizontal (side-to-side) disturbance (S-waves) which further increases magnitude and intensity. The P and S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of wave forms travelling at different speeds – the first to arrive are vertical (up / down) disturbances (P-waves) which are followed by a horizontal (side-to-side) disturbance (S-waves) which further increases magnitude and intensity.

•

• Earthquakes are created either when stratigraphic units either side of a geological fault are displaced in relation to each other - or when either Continental or Oceanic Plates moving relative to each other get stuck together. Over time stress builds up at the fault-line, causing them to dramatically unzip – then slip and slide over each other - releasing a sequence of energetic tremors or waves. P-waves oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of waves - but travelling at different speeds. The first waves to arrive at an observer of the event are vertical (up / down) disturbances (P-waves) which are followed moments later by a horizontal (side-to-side) disturbance (S-waves) which have increased magnitude and intensity.

•

• P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster P-waves are followed by slower but more dramatic and intense S-waves – these Strong Signals are now giving indications of what is about to come. Next in the sequence comes a Wild Card Event. At the nearest coastline, the sea level first falls as the Tsunami Wave withdraws water from the shore – this is the final warning, the Wild Card Event. Finally the tsunami arrives as a Black Swan Event. This is the last Wave in the sequence. The chaotic and radically disruptive Tsunami wave travels through the ocean waters and arrives as the final Black Swan Event. Surging relentlessly inland, threatening life and shifting scenery, the catastrophic disturbance of the Tsunami wave sweeps up everything that lies in its way, a black wave swallowing everything before it. This type of Black Swan Event has occurred twice this century - the Boxing Day and Japanese Tsunamis.

•


• • Randomness. Weak Signals, Wild Cards and Black Swan Events – may be evidence of disruptive and chaotic Random Events which propagate through the Space-Time Continuum in the

same way as a ripple or wave. Random Events may be able to “bend” the Space-Time continuum- which brings two discrete points on different Hyperspace Planes closer together over a time interval extended through a time-line along the Time axis of the Minkowski Space-Time Continuum. Perhaps some Wave Types - Weak Signals, Wild Cards and Black Swan Events - can travel faster or take a different route – perhaps because their Wave forms can propagate through Space-Time more rapidly than the Wave forms of the other types of Wave - or specific Wave Types are able to take a “short cut” between two points on different Hyperspace Planes.

• • • CASE STUDY • A Pyroclastic Volcanic Eruption begins with a series of linked and integrated events which have a common origin or source - which in turn generate a sequence of

waves in ascending orders of magnitude. Pyroclastic Volcanic Eruptions begin with a sequence of Random Events - in this case, it is a sequence of chaotic and disruptive Earthquakes somewhere deep under an Mountain Chain which is built up from Andesitic Volcanoes – such as the Andes Mountain Chain. The Andes Mountains are located near an Oceanic Plate subduction zone – an area where the Pacific Oceanic Plate plunges under the South American Continental Margin. Sediment, sea water and organic remains from the Ocean floor are carried down towards earth’s mantle and heat up as the Oceanic Plate plunges deeper into the Earth’s Mantle. Liquids and gases released by this heating cause the rocks in the Earth’s Mantle to melt, turning from a plastic semi-solid into a liquid. This liquid then rises through the Earth’s crust and travels towards the surface, collecting in pools forming Magma Chambers - before finally breaking at the surface through and erupting as Volcanic Magma

• • Earthquakes are created when the Continental and Oceanic Plates stick together – and periodically unzip and slide over each other - causing a sequence of tremors or waves. P-waves

oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of waves - but travelling at different speeds. The first waves to arrive at an observer of the event are vertical (up / down) disturbances (P-waves) which are followed moments later by a horizontal (side-to-side) disturbance (S-waves) which have increased magnitude and intensity.

• • P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster P-waves are followed by slower but more dramatic and intense S-waves – these Strong Signals

are now giving indications of what is about to follow. Next in the sequence is the Wild Card Event. As the volcano erupts, its ash cloud billows high into the atmosphere. Finally the Black Swan Event arrives. As the volcano continues to erupt, the ash column can no longer support its own weight. It collapses in onto itself and plunges down the slopes of the Volcano. Surging relentlessly downhill, the catastrophic disturbance of the Pyroclastic wave destroys all life and covers the landscape with layers of suffocating, burning hot ash, a black wave covering over everything that lies before it. This, for example, is what happened when Vesuvius erupted and covered Herculaneum and Pompeii with over twenty metres of superheated magma erupting as gases and ash.

• • • CASE STUDY • A Tsunami Event consists of a sequence of linked and integrated waves in ascending orders of magnitude which have a common origin or source – in this case, the

Random Events begin with a series of chaotic and disruptive Earthquakes somewhere offshore in a subduction zone at a Continental and Oceanic Plate Margin. Earthquakes are formed as the Continental and Oceanic Plates stick together – and then unzip, causing a sequence of random and chaotic tremors. The P and S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of waves travelling at different speeds – the first to arrive are vertical (up / down) disturbances (P-waves) which are followed by a horizontal (side-to-side) disturbance (S-waves) which further increases magnitude and intensity. The P and S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of wave forms travelling at different speeds – the first to arrive are vertical (up / down) disturbances (P-waves) which are followed by a horizontal (side-to-side) disturbance (S-waves) which further increases magnitude and intensity.

• • Earthquakes are created either when stratigraphic units either side of a geological fault are displaced in relation to each other - or when either Continental or Oceanic Plates moving

relative to each other get stuck together. Over time stress builds up at the fault-line, causing them to dramatically unzip – then slip and slide over each other - releasing a sequence of energetic tremors or waves. P-waves oscillate up-and-down, whilst S-waves oscillate from side-to-side. The P and S waves from the Earthquake propagate rapidly through the earth in a related and integrated series of waves - but travelling at different speeds. The first waves to arrive at an observer of the event are vertical (up / down) disturbances (P-waves) which are followed moments later by a horizontal (side-to-side) disturbance (S-waves) which have increased magnitude and intensity.

• • P-waves travel fastest through the earth so they arrive first, as Weak Signals. These faster P-waves are followed by slower but more dramatic and intense S-waves – these Strong Signals

are now giving indications of what is about to come. Next in the sequence comes a Wild Card Event. At the nearest coastline, the sea level first falls as the Tsunami Wave withdraws water from the shore – this is the final warning, the Wild Card Event. Finally the tsunami arrives as a Black Swan Event. This is the last Wave in the sequence. The chaotic and radically disruptive Tsunami wave travels through the ocean waters and arrives as the final Black Swan Event. Surging relentlessly inland, threatening life and shifting scenery, the catastrophic disturbance of the Tsunami wave sweeps up everything that lies in its way, a black wave swallowing everything before it. This type of Black Swan Event has occurred twice this century - the Boxing Day and Japanese Tsunamis.

•


• Nothing in the galaxy, in our world, or in our own personal existence, ever happens in isolation of other places, objects, individuals and events. This is a very simple and fundamental fact about life, nature and the universe. In the same moment as one event occurs or happens in one location, infinite other events are simultaneously taking place in countless other locations, which in turn impact on an innumerable collection of other co-impacted objects, individuals and events.

• In order to study and prepare for the future - we need to bear in mind the fact that all objects and events are potentially connected in some way or other. None of these random events occurs in isolation, none are entirely independent or unconnected – as every object in the Universe exerts an influence over every other object – however tenuously. This phenomenon of Event Clustering is something that, through our own experience, we are all familiar with, know about, and can easily follow and understand.


• It is the dream of every Futurist to be the first to discover an important and significant chain of random events which occur together in clusters, that together demonstrate a transient or instantiated dependencies - that is, they are interacting or co-related so they are impacting upon each other in some way or another. Fundamentally - we need to apply systems thinking to analyse a combination or sequence of random events in order to resolve Future Domain problems or opportunities, threats, issues or challenges.

• What factors or forces do we need to consider as being in-scope and critical to the behaviour of the system we are studying? Which other factors or forces have we ignored, overlooked or simply just not considered? What further unknown factors or unseen forces are there which we have not detected – but may still exist – which are impacting on or interacting with various system elements and thus influencing the behaviour of the system?

Weak Signals, Wild Cards and

Black Swan Events • In this section, we examine empiric evidence from global “Big Data” on how shock waves

to geo-political economic and business systems impact on business cycles, patterns and

trends. We first review Gail's work (1999), which uses long-running restrictions to identify

shock waves, and examine whether the identified shocks can be plausibly interpreted: -

• Wild card and Black Swan Events



– Impact of War, Terrorism and Insecurity

• We do this in three ways. Firstly, we derive additional long-run restrictions and use them

as identification tests. Secondly, we compare the qualitative implications from the model

with the impulse responses of variables such as production, wages and consumption.

Third, we test whether some standard .exogenous. variables predicate the shock events.

We discovered that that Weak Signals may predicate coming technology shock waves, oil

price shocks, and military conflict. We then show ways in which a standard DGE model

can be modified to fit Gail's finding that a positive technology shock may lead to lower

labour input. Finally, we re-examine the properties of the other key shocks to the economic

system and demonstrate the impact of oil price shocks and military conflict .

Price Index Inflation

Waves, Cycles, Patterns and Trends

• Business Cycles were once thought to be an economic phenomenon due to periodic fluctuations in economic activity. These mid-term economic cycle fluctuations are usually measured using Real (Austrian) Gross Domestic Product (rGDP). Business Cycles take place against a long-term background trend in Economic Output – growth, stagnation or recession – which affects Money Supply as well as the relative availability and consumption (Demand v. Supply and Value v. Price) of other Economic Commodities. Any excess of Money Supply may lead to an economic expansion or “boom”, conversely shortage of Money Supply may lead to economic contraction or “bust”. Business Cycles are recurring, fluctuating levels of economic activity experiences in an economy over a significant timeline (decades or centuries).

• The five stages of Business Cycles are growth (expansion), peak, recession (contraction), trough and recovery. Business Cycles were once widely thought to be extremely regular, with predictable durations, but today’s Global Market Business Cycles are now thought to be unstable and appear to behave in irregular, random and even chaotic patterns – varying in frequency, range, magnitude and duration. Many leading economists now also suspect that Business Cycles may be influenced by fiscal policy as much as market phenomena - even that Global Economic “Wild Card” and “Black Swan” events are actually triggered by Economic Planners in Government Treasury Departments and in Central Banks as a result of manipulating the Money Supply under the interventionalist Fiscal Policies adopted by some Western Nations.

Random Event Clustering

Patterns in the Chaos


• Complex Systems and Chaos Theory has been used extensively in the field

of Futures Studies, Strategic Management, Natural Sciences and Behavioural

Science. It is applied in these domains to understand how individuals within

populations, societies, economies and states act as a collection of loosely

coupled interacting systems which adapt to changing environmental factors

and random events – bio-ecological, socio-economic or geo-political.

• Complex Systems and Chaos Theory treats individuals, crowds and

populations as a collective of pervasive social structures which are influenced

by random individual behaviours – such as flocks of birds moving together in

flight to avoid collision, shoals of fish forming a “bait ball” in response to

predation, or groups of individuals coordinating their behaviour in order to

respond to external stimuli – the threat of predation or aggression – or in order

to exploit novel and unexpected opportunities which have been discovered or

presented to them.


• The defining concept for understanding the effects of Chaos Theory on Complex Systems is that with

any vanishingly small differences in the initial conditions at the onset of a chaotic system cycle – those

minute and imperceptible differences which create slightly different starting points result in massively

different outcomes between two otherwise identical systems, both operating within the same time frame.

• The discovery of Chaos and Complexity has increased our understanding of the Cosmos and its effect

on us. If you surf the chaos content regions of the internet, you will invariably encounter terms such as: -

• These influences can take some time to manifest themselves, but that is the nature of the phenomena

identified as a "strange attractor." Such differences could be small to the point of invisibility - how tiny

can influences be to have any effect? This is captured in the “butterfly scenario” described below.


13. Phase space and locking 14. Randomness 15. Sensitivity to initial conditions 16. Self similarity (self affinity) 17. Starting conditions 18. Stochastic events 19. Strange attractors 20. System cycles (iterations) 21. Time-series Events 22. Turbulence 23. Uncertainty 24. Vanishingly small differences



• Weaver (Complexity Theory) along with Gleick and Lorenzo (Chaos Theory) have given us some of the tools that we need to understand these complex, interrelated chaotic and radically disruptive political, economic and social events such as the collapse of Global markets – and the various protests against this - using Event Decomposition, Complexity Mapping, and Statistical Analysis to help us identify patterns, extrapolations, scenarios and trends unfolding as seemingly unrelated, random and chaotic events. The Hawking Paradox, however, challenges this view of Complex Systems by postulating that uncertainty dominates complex, chaotic systems to such an extent that future outcomes are both unknown - and unknowable.

• System Complexity is typically characterised by the number of elements in a system, the number of interactions between those elements and the nature (type) of interactions. One of the problems in addressing complexity issues has always been distinguishing between the large number of elements and relationships, or interactions evident in chaotic (disruptive, unconstrained) systems - and the still large, but significantly smaller number of elements and interactions found in ordered (constrained) systems. Orderly (constrained) System Frameworks tend to act to both reduce the total number of more-uniform elements and interactions with fewer regimes and of reduced size – and feature explicit rules which govern less random and chaotic, but more highly-ordered, internally correlated and constrained interactions – as compared with the massively increased random, chaotic and disruptive behaviour exhibited by Disorderly (unconstrained) System Frameworks.









interactions – with fewer and smaller classes of more uniform elements – with reduced and




Simplexity Ordered

Complexity

Disordered


Systems (CAS)

Linear

Systems


Chaos Order



often defined as consisting of a small number of relatively simple and loosely connected

systems - then they are much more likely to adapt to their environment and, thus,

survive the impact of change and random events. Complexity Theory thinking has been

present in strategic and organisational studies since the first inception of Complex

Adaptive Systems (CAS) as an academic discipline.












• Complex Systems and Chaos Theory has been used extensively in the field

of Futures Studies, Strategic Management, Natural Sciences and Behavioural

Science. It is applied in these domains to understand how individuals within

populations, societies, economies and states act as a collection of loosely

coupled interacting systems which adapt to changing environmental factors

and random events – bio-ecological, socio-economic or geo-political.

• Complex Systems and Chaos Theory treats individuals, crowds and

populations as a collective of pervasive social structures which are influenced

by random individual behaviours – such as flocks of birds moving together in

flight to avoid collision, shoals of fish forming a “bait ball” in response to

predation, or groups of individuals coordinating their behaviour in order to

respond to external stimuli – the threat of predation or aggression – or in order

to exploit novel and unexpected opportunities which have been discovered or

presented to them.


• In a crowd of human beings or a swarm of animals, individuals are so closely connected that they share the same mood and emotions (fear, greed, rage) and demonstrate the same or very similar behaviour (fight, flee or feeding frenzy). Only the first few individuals exposed to the Causal Event or incident may at first respond strongly and directly to the initial “trigger” stimulus, causal event or incident (opportunity or threat – such as external predation, aggression or discovery of a novel or unexpected opportunity to satisfy a basic need – such as feeding, reproduction or territorialism).

• Those individuals who have been directly exposed to the initial “trigger” event or incident - the system input or causal event that initiated a specific outbreak of behaviour in a crowd or swarm – quickly communicate and propagate their swarm response mechanism and share with all the other individuals – those members of the Crowd immediately next to them – so that modified Crowd behaviour quickly spreads from the periphery or edge of the Crowd.

• Peripheral Crowd members in turn adopt the Crowd response behaviour without having been directly exposed to the “trigger”. Most members of the crowd or swarm may be totally oblivious as to the initial source or nature of the trigger stimulus - nonetheless, the common Crowd behaviour response quickly spreads to all of the individuals in or around that crowd or swarm.

Randomness Patterns in the Chaos





• The defining concept for understanding the effects of Chaos Theory on Complex Systems is that with

any vanishingly small differences in the initial conditions at the onset of a chaotic system cycle – those

minute and imperceptible differences which create slightly different starting points result in massively

different outcomes between two otherwise identical systems, both operating within the same time frame.

• The discovery of Chaos and Complexity has increased our understanding of the Cosmos and its effect

on us. If you surf the chaos content regions of the internet, you will invariably encounter terms such as: -

• These influences can take some time to manifest themselves, but that is the nature of the phenomena

identified as a "strange attractor." Such differences could be small to the point of invisibility - how tiny

can influences be to have any effect? This is captured in the “butterfly scenario” described below.


13. Phase space and locking 14. Randomness 15. Sensitivity to initial conditions 16. Self similarity (self affinity) 17. Starting conditions 18. Stochastic events 19. Strange attractors 20. System cycles (iterations) 21. Time-series Events 22. Turbulence 23. Uncertainty 24. Vanishingly small differences



• Weaver (Complexity Theory) along with Gleick and Lorenzo (Chaos Theory) have given us some of the tools that we need to understand these complex, interrelated chaotic and radically disruptive political, economic and social events such as the collapse of Global markets – and the various protests against this - using Event Decomposition, Complexity Mapping, and Statistical Analysis to help us identify patterns, extrapolations, scenarios and trends unfolding as seemingly unrelated, random and chaotic events. The Hawking Paradox, however, challenges this view of Complex Systems by postulating that uncertainty dominates complex, chaotic systems to such an extent that future outcomes are both unknown - and unknowable.

• System Complexity is typically characterised by the number of elements in a system, the number of interactions between those elements and the nature (type) of interactions. One of the problems in addressing complexity issues has always been distinguishing between the large number of elements and relationships, or interactions evident in chaotic (disruptive, unconstrained) systems - and the still large, but significantly smaller number of elements and interactions found in ordered (constrained) systems. Orderly (constrained) System Frameworks tend to act to both reduce the total number of more-uniform elements and interactions with fewer regimes and of reduced size – and feature explicit rules which govern less random and chaotic, but more highly-ordered, internally correlated and constrained interactions – as compared with the massively increased random, chaotic and disruptive behaviour exhibited by Disorderly (unconstrained) System Frameworks.









interactions – with fewer and smaller classes of more uniform elements – with reduced and




Simplexity Ordered

Complexity

Disordered


Systems (CAS)

Linear

Systems


Chaos Order



often defined as consisting of a small number of relatively simple and loosely connected

systems - then they are much more likely to adapt to their environment and, thus,

survive the impact of change and random events. Complexity Theory thinking has been

present in strategic and organisational studies since the first inception of Complex

Adaptive Systems (CAS) as an academic discipline.











Complexity Map


• There are many kinds of stochastic or random processes that impacts on every area of

Nature and Human Activity. Randomness can be found in Science and Technology and in

Humanities and the Arts. Random events are taking place almost everywhere we look – for

example from Complex Systems and Chaos Theory to Cosmology and the distribution and

flow of energy and matter in the Universe, from Brownian motion and quantum theory to

Fractal Branching and linear transformations. Further examples include Random Events,

Weak Signals and Wild Cards occurring in each aspect of Nature and Human Activity – from

Ecology and the Environment to Weather Systems and Climatology in Economics and in the

Biological basis of Behaviour. And then there are the examples of atmospheric turbulence,

and the complex orbital and solar interaction cycles – and much, much more than this.....

• There is an interesting phenomenon called Phase Locking where two loosely coupled

systems with slightly different frequencies show a tendency to move into resonance – in order

to harmonise with one another. We also know that the opposite of system convergence -

system divergence - is also possible with phase-locked systems, which can also diverge with

only very tiny inputs - especially if we run those systems in reverse. Thus phase locking

draws two nearly harmonic systems into resonance and gives us the appearance of a

“coincidence”. There are, however, no coincidences in Physics. Sensitive Dependence in

Complexity Theory also tells us that minute, imperceptible changes to inputs at the initial state

of a system, at the beginning of a cycle, are sufficient to dramatically alter the final state after

even only a few iterations of the system cycle.

Wave-form Analytics in Economic Cycles

• A fundamental challenge found everywhere in business cycle theory is how to interpret very large scale / long period compound-wave (polyphonic) time series data sets which are dynamic (non-stationary) in nature. Wave-form Analytics is a new analytical too based on Time-frequency analysis – a technique which exploits the wave frequency and time symmetry principle. Trend-cycle decomposition is a critical technique for testing the validity of multiple compound dynamic wave-form models in a complex array of interacting and inter-dependant cyclic systems. In order to study complex periodic economic phenomena there are a number of competing analytic paradigms – which are driven by either deterministic methods (goal-seeking - testing the validity of a range of explicit / pre-determined / pre-selected cycle periodicity value) and stochastic (random / probabilistic / implicit - testing every possible wave periodicity value - or by identifying actual wave periodicity values from the “noise” – harmonic resonance and interference patterns).

• The Wigner-Gabor-Qian (WGQ) spectrogram demonstrates a distinct capability for revealing complex cycles within dynamic, noisy and unstable time-series data sets. A variety of competing deterministic and stochastic methods, including the first difference (FD) and Hodrick-Prescott (HP) filter - may be deployed with the multiple-frequency mixed case of overlaid cycles and system noise. The FD filter does not produce a clear picture of business cycles – however, the HP filter provides us with strong results for pattern recognition of multiple co-impacting business cycles. The existence of stable characteristic frequencies in large economic data aggregations (“Big Data”) provides us with strong evidence and valuable information about the structure of Business Cycles.

Wave-form Analytics in Economic Cycles

• Economic systems demonstrate Complex Adaptive System (CAS) behaviour -

more similar to an organism than to Chaotic Disorderly System “Random Walks”.

The remarkable adaptability, stability and resilience of market economies may be

demonstrated by the impact of Black Swan Events causing stock market crashes

- such as oil price shocks (1970-72) and credit supply shocks (1927- 1929 and

2008 onwards).

• Unexpected and surprising Cycle Pattern changes have occurred during wars,

and technology innovation-driven arms races - and also during the Reagan

Republican administration (why?). Just as advances in electron microscopy

revolutionised biology - non-stationary time series wave-form analysis has

opened up a new space for business cycle studies and economic policy

diagnostics.

• The role of time scale and preferred reference from economic observation are

fundamental constraints for Friedman's rational arbitrageurs and will be re[-

examined from the viewpoint of information ambiguity and dynamic instability.

Quantitative (Technical) Analysis

• Quantitative (Technical) Analysis involves studying detailed micro-economic models which process vast quantities of Market Data (commodity price data sets). This method utilises a form of historic data analysis technique which smoothes or profiles market trends into more predictable short-term price curves - which will vary over time within a specific market.

• Quantitative (Technical) Analysts can initiate specific market responses when prices reach support and resistance levels – via manual information feeds to human Traders or by tripping buying or selling triggers where autonomous Computer Trading is deployed. Technical Analysis is data-driven (experiential), not model-driven (empirical) because our current economic models do not support the observed market data. The key to both approaches, however, is in identifying, analysing, and anticipating subtle changes in the average direction of movement for Price Curves – which in turn reflect relatively short-term Market Trends.

Quantitative / Qualitative Analysis Techniques

TECHNICAL (QUANTITATIVE) METHODS TECHNICAL (QUANTITATIVE) METHODS (cont.)

Asymptotic Methods and Perturbation Theory Statistical Arbitrage

“Big Data” - Statistical analysis of very large scale (VLS) datasets Technical (Quant) Analysis

Capital Adequacy – Liquidity Risk Modelling – Basle / Solvency II Trading Strategies - neutral, HFT, pairs, macro; derivatives;

Convex analysis Trade Risk Modelling: – Risk = Market Sentiment – Actual Results

Credit Risk Modelling (PD, LGD) Value-at-Risk (VaR)

Data Audit, Data Profiling. Data Mining and CHAID Analysis Volatility modelling (ARMA, GARCH)

Derivatives (vanilla and exotics)

Dynamic systems behaviour and bifurcation theory NARRATIVE (QUALITATIVE) METHODS

Dynamic systems complexity mapping and network reduction

Differential equations (stochastic, parabolic) “Big Data” -, Clinical Trials ,Morbidity and Actuarial Outcomes

Extreme value theory Business Strategy, Planning, Forecasting Simulation and Consolidation

Economic Growth / Recession Patterns (Boom / Bust Cycles) Causal Layer Analysis (CLA)

Economic Planning and Long-range Forecasting Chaos Theory

Economic Wave and Business Cycle Analysis Cluster Theory

Financial econometrics (economic factors and macro models) Complexity Theory

Financial time series analysis Complex (non-linear) Systems

Game Theory and Lanchester Theory Complex Adaptive Systems (CAS)

Integral equations Computational Theory (Turing)

Interest rates derivatives Delphi Oracle /Expert Panel / Social Media Survey

Ordered (Linear) Systems (simple linear multi-factor equations) Economic Wave Theory – Business Cycles (Austrian School)

Market Risk Modelling (Greeks; VaR) Fisher-Pry Analysis and Gomperttz Analysis

Markov Processes Forensic “Big Data” – Social Mapping and Fraud Detection

Monte Carlo Simulations and Cluster Analysis Geo-demographic Profiling and Cluster Analysis

Non-linear (quadratic) equations Horizon Scanning, Monitoring and Tracking

Neural networks, Machine Learning and Computerised Trading Information Theory (Shannon)

Numerical analysis & computational methods Monetary Theory – Money Supply (Neo-liberal and Neo-classical)

Optimal Goal-seeking, System Control and Optimisation Pattern, Cycle and Trend Analysis

Options pricing (Black-Scholes; binomial tree; extensions) Scenario Planning and Impact Analysis

Price Curves – Support / Resistance Price Levels - micro models Social Media – market sentiment forecasting and analysis

Quantitative (Technical) Analysis Value Chain Analysis – Wealth Creation and Consumption

Statistical Analysis and Graph Theory Weak Signals, Wild Cards and Black Swan Event Forecasting

Qualitative (Narrative) Analysis

• Qualitative (Narrative) Analysis involves further processing of summarised results generated by Quantitative (Technical) Analysis - super sets of many individual micro-economic model runs. Techniques such as Monte Carlo Simulation cycle macro-economic model runs repeatedly through thousands of iterations – minutely varying the starting conditions for each and every individual run cycle.

• Results appear as a scatter diagram consisting of thousands of individual points for commodity prices over a given time line. Instead of a random distribution – we discover clusters of closely related results in a background of a few scattered outliers. Each of these clusters represents a Scenario – which is analysed using Cluster Analysis methods - Causal Layer Analysis (CLA), Scenario Planning and Impact Analysis– where numeric results are explained as a narrative story about a possible future outcome – along with the probability of that scenario materialising.


• Every Disruptive Futurist is seeking to discover a combination, sequence or chain of events which occur together in clusters, and when acting together demonstrate transient or instantiated dependencies (are interacting or co-related) – that is, they are impacting upon each other in some way or another.. Basically, we need to apply systems thinking to resolving Future Domain problems, opportunities, threats, issues or challenges.

• What factors or forces do we need to consider as being in-scope and critical to the behaviour of the system? Which other factors or forces have we ignored, overlooked or not considered? What further unknown factors or unseen forces are there which we have not detected – but may still exist – which are somehow exerting influence over the behaviour of the system - thus impacting on system elements through Space and Time?


Multiple Random processes also occur in clusters.....

• Random Processes (with the notable exception of Quantum Events) are

never truly nor completely random nor symmetrical – they are triggered by the

manifestation of “unseen forces”. It is the nature of Random Processes –

which tend to occur together in multiple, related and similar sequences – to

generate rare Chaotic Events – which have a tendency to occur in clusters.

• At the local level, we see stochastic processes at work when we experience

the myriad of phenomena that make up our everyday life experiences. Almost

without exception, we hear of events by type occurring close together in

temporal and spatial proximity. The saying that bad or good news comes in

groups has some validity based upon the nature of event clustering. Plane,

train or bus crashes come in groups spaced close together in time, separated

by long periods of no such events.

The Butterfly Effect

• Weather prediction is an extremely difficult problem. Meteorologists can predict the weather for short periods of time, a couple days at most, but beyond that predictions are generally poor.

• Edward Lorenz was a mathematician and meteorologist at the Massachusetts Institute of Technology who loved the study of weather. With the advent of computers, Lorenz saw the chance to combine mathematics and meteorology. He set out to construct a mathematical model of the weather, namely a set of differential equations that represented changes in temperature, pressure, wind velocity, etc. In the end, Lorenz stripped the weather down to a crude model containing a set of 12 differential equations.

• On a particular day in the winter of 1961, Lorenz wanted to re-examine a sequence of data coming from his model. Instead of restarting the entire run, he decided to save time and restart the run from somewhere in the middle. Using data printouts, he entered the conditions at some point near the middle of the previous run, and re-started the model calculation. What he found was very unusual and unexpected. The data from the second run should have exactly matched the data from the first run. While they matched at first, the runs eventually began to diverge dramatically — the second run losing all resemblance to the first within a few "model" months. A sample of the data from his two runs in shown below:


• At first Lorenz thought that a vacuum tube had gone bad in his computer, a Royal McBee — an extremely slow and crude machine by today's standards. After discovering that there was no malfunction, Lorenz finally found the source of the problem. To save space, his printouts only showed three digits while the data in the computer's memory contained six digits. Lorenz had entered the rounded-off data from the printouts assuming that the difference was inconsequential. For example, even today temperature is not routinely measured within one part in a thousand.

• This led Lorenz to conclude that detailed long-term weather forecasting was doomed. His simple model exhibits the phenomenon known as "sensitive dependence on initial conditions." This is sometimes referred to as the Butterfly Effect, e.g. a butterfly flapping its wings in South America can affect the weather in Central Park.

• The question then arises — why does a set of completely deterministic equations exhibit this chaotic behaviour? After all, scientists are often taught that small initial perturbations lead to small changes in model behaviour. This was clearly not the case in Lorenz's model of the weather – where small initial perturbations in temperature lead to massive changes in weather model behaviour. The answer lies in the nature of the equations; they were nonlinear equations. While they are difficult to solve, nonlinear systems are central to chaos theory and often exhibit fantastically complex and chaotic behaviour.


• Extremes in weather follow a similar stochastic pattern. Everyone is familiar

with the expression "When it rains, it pours”.....

• Lorenz's initial weather model exhibited chaotic behaviour, which involved a set

of 12 nonlinear differential equations. Lorenz decided to look for complex

behaviour in an even simpler set of equations, and was led to the phenomenon

of rolling fluid convection. The physical model is simple: place a gas in a solid

rectangular box with a heat source on the bottom.

• Lorenz then simplified several fluid dynamics equations (called the Navier-

Stokes equations) and from the original twelve nonlinear equations ended up

with a simplified set of just three nonlinear equations: -

• Where P is the Pr and tl number representing the ratio of the fluid viscosity to

its thermal conductivity, R represents the difference in temperature between the

top and bottom of the system, and B is the ratio of the width to height of the box

used to hold the system. The values Lorenz used are P = 10, R = 28, B = 8/3.




• Sensitive Dependence in Complexity Theory tells us that minute, imperceptible changes

to a system – at the beginning of a cycle, or dynamic forces acting as the cycle evolves -

are sufficient to dramatically alter the final state of the system - even after a relatively few

iterations of the system cycle.. Changes to a system at the initial state constitutes Initial

Sensitive Dependence, whilst dynamic external forces acting on the system as the cycle

evolves constitutes Dynamic Sensitive Dependence. Thus Attractors and Repellents

are examples of Dynamic Sensitive Dependence.

• Any trajectory of the dynamic system in the attractor does not have to satisfy any special

constraints - except for remaining as an attractor. The trajectory may be periodic or

chaotic. In a set of periodic or chaotic points, if the average flow in the neighbourhood is

generally towards the set, then it is an attractor. If the average neighbourhood flow is

generally away from the set, then that set is instead referred to as a repellent (repellor)..

http://en.wikipedia.org/wiki/Trajectory

http://en.wikipedia.org/wiki/Periodic_function


http://en.wikipedia.org/wiki/Repellor



• An attractor is a set within a dynamic system, towards which a moving variable

evolves over time. That is, points in that set get close enough to the attractor to

remain close - even when slightly disturbed by an external force The evolving

time-variant variable may be represented algebraically as an n-dimensional vector.

• The attractor is a region in n-dimensional space. In physical systems, the n

dimensions may be, for example, three positional coordinates and one temporal

co-ordinate for each of one or more physical entities; in economic systems, they

may be separate variables such as the inflation rate and the unemployment rate.

• If the evolving variable is two- or three-dimensional, the attractor of the dynamic

process can be represented geometrically in two or three dimensions, (as for

example in the three-dimensional case depicted to the right). An attractor can be

a point, a finite set of points, a curve, a manifold, or even a complicated set with

a fractal structure known as a strange attractor. If the variable is a scalar, the

attractor is a subset of the real number line. Describing attractors in dynamic

chaotic systems has been one of the greatest achievements of chaos theory.

http://en.wikipedia.org/wiki/Set_(mathematics)

http://en.wikipedia.org/wiki/Dynamical_system

http://en.wikipedia.org/wiki/Variable_(mathematics)

http://en.wikipedia.org/wiki/Algebra

http://en.wikipedia.org/wiki/Coordinate_vector




http://en.wikipedia.org/wiki/Physics

http://en.wikipedia.org/wiki/Economics

http://en.wikipedia.org/wiki/Inflation_rate

http://en.wikipedia.org/wiki/Unemployment_rate

http://en.wikipedia.org/wiki/Geometry

http://en.wikipedia.org/wiki/Point_(geometry)

http://en.wikipedia.org/wiki/Curve

http://en.wikipedia.org/wiki/Manifold


http://en.wikipedia.org/wiki/Attractor

http://en.wikipedia.org/wiki/Scalar_(mathematics)



Strange Attractor

• A Strange Attractor has a fractal dimensional structure. This is often the case when

the system dynamics are chaotic. Strange attractors that are non-chaotic may also

exist. The term Strange Attractor was coined by David Ruelle and Floris Takens to

describe the attractor resulting from a series of bifurcations in a system modelling the

heat convection dynamics of a fluid heated from below and cooled at the top – this

process drives Plate Tectonics in the Earths mantle – causing Continental Drift.

• Strange attractors are often differentiable in a few directions, but some are like

Cantor dust, and are therefore not differentiable. Strange attractors may also be

found in presence of noise - where they may be shown to support invariant Random

Probability measures of Sinai-Ruelle-Bowen type - see Chekroun et al. (2011).

• A Strange Attractor is an attracting set that has zero measure in the embedding

phase space and has fractal dimensions. Trajectories within a strange attractor

appear to skip around randomly. On the surface these three equations seem

relatively simple to solve. However, they represent an extremely complicated and

variable dynamic system. If the results are plotted in three dimensions, then the

following three-dimensional figure, called the Lorenz attractor, is obtained: -








http://en.wikipedia.org/wiki/Bifurcation_theory

http://en.wikipedia.org/wiki/Differentiable

http://en.wikipedia.org/wiki/Homeomorphic

http://en.wikipedia.org/wiki/Cantor_dust

http://mathworld.wolfram.com/Measure.html


http://mathworld.wolfram.com/Fractal.html


• Phase Divergence drives two phase-locked harmonic systems out of

synchronisation into random, chaotic and discordant behaviour - where phase

locked systems can diverge from each other with only very tiny inputs (especially

when we run those phase-locked harmonic system models in reverse).....

• Due to the fact that very complex systems are invoked, it is safe to say that pure

coincidence is a vanishingly small reality. In fact, it is safe to say that phenomena

such as coincidence (which is more properly called serendipity)– as seen drawing

two interacting bodies into perfect resonance - is due to unknown factors or unseen

forces behind effects such as phase locking, and sensitive dependence. Sensitive

dependence and the interaction of every object upon all of the rest accounts for the

phenomenon of clustering – not serendipity, coincidence or mere chance.....

• The structure of the universe is based on such stochastic events. Here too, we find

random clustering events. The distribution of matter in the universe is based on the

quantum foundation. Clustering at the quantum level when the universe was just a

few thousands of a millimetre across – has lead to the creation of the super massive

black holes at the centre of each galaxy which, through gravitational attraction drive

the clustering of star / planetary systems, star clusters, galaxies and galactic clusters.



• There is another interesting phenomenon called Phase Locking where two

loosely coupled systems with slightly different frequencies show a tendency to

move into resonance – they are seeking to harmonise with one another. We also

know that the opposite of system convergence - system divergence - is also

possible with phase-locked systems, Sensitive dependence also tells us that very

tiny inputs are enough to completely alter the final state after several iterations of

the dynamic. We thus know of systems that diverge with only very tiny inputs, but

the opposite is also true with convergence, especially if we run things in reverse.

• Thus phase locking draws two nearly harmonic systems into resonance and gives

us the appearance of a “coincidence”. There are, however, no coincidences in

nature or Physics - all random processes (with the notable exception of Quantum

Events) – are neither truly random nor completely symmetrical – but are simply the

outcome of unseen forces acting on the system. Such 'coincidences' are like the

clusters of personality types that are governed by certain recurring planets -

according to the statistical researches of M. Guaquelin.





• Even when we look to the formation of solar systems, we see evolution

mediated by forces, random events and harmonics in synchronicity. Since

random events tend to cluster as part of the natural evolution of the cosmos, it is

not surprising to find that complex systems will evolve as a natural

consequence. Planets in orbit around a star must have orbital periods in

dissonance to each other in order to have reasonable stability.

• This dissonance will evolve through time to create patterns that occur randomly

in time where planets cluster along one line of sight or another. Such is the

nature of great stelliums. In our solar system, this kind of thing occurs roughly

once very forty years, but no two stelliums are alike in planetary grouping, angle

of spread or where they are in reference to the background stars. Since planets

orbit in more or less well-defined periods, these events are highly predictable,

unlike the events in the quantum realm or with coin tosses in a chained

sequence of events.



• The long horizon of predictability of planetary alignments can allow us to

determine when events associated with the planets will tend to cluster as well.

Planetary clustering in an non-periodic fashion will generate non-periodic

effects, each object impacting upon all others. Earth is not exempt from the

forces of these planets. They manifest in many ways, obvious and subtle. Some

are easy to understand, others are not.

• We can calculate the perturbation and tidal influences with some ease and

match these with real effects we experience. The psychic influences are much

harder to track. They are there nonetheless as evidenced by the lunar and solar

influences. The stochastic and clustering nature of these influences is what is

behind the seeming stochastic and clustering nature of events we experience.

THE ELTVILLE MODEL

SIX VISIONS OF THE FUTURE – THE ELTVILLE MODEL

There are six viewpoints or lenses from which we may understand the future: - 1. BLUE lenses are for PROBABLISTIC FUTURE – RATIONAL FUTURISTS

2. RED lenses are for FUTURE THREATS – DISRUPTIVE FUTURISTS

3. GREEN lenses are for FUTURE OPPORTUNISTIIES – EVOLUTIONARY FUTURISTS

4. GOLD lenses are for DESIRED FUTURE VISION – GOAL ANALYSTS

5. INDIGO lenses are for STEADY STATE FUTURES – EXTRAPOLATION / PATTERN ANALYSTS

6. The VIOLET lenses are for a DETERMINISITC FUTURE – STRATEGIC POSITIVISTS

• Many of the issues that we encounter in Future Management Studies – from driving

Private-sector strategic management to formulating Government Political, Economic and

Social Policies - result from attempts to integrate multiple viewpoints from different

people. Everybody subconsciously believes that other people thinks about, articulates and

understands the Future Narrative in exactly the same way as they do. Stakeholders often

tend to assume that everyone else is looking through the same ”futures lenses” - which

may lead to misunderstanding, conflict, frustration or failure.

• The Eltville Model consists of a process model that describes six different viewpoints or

perspectives of the future (the “six futures lenses") - as a sequence of mental steps (for

exploration and discovery in a workshop) environment, and a results model, which

captures the results achieved in the process as "thought objects“.

The SIX futures lenses below make it easier to analyse and understand the future: -

1. BLUE lenses are for PROBABLISTIC FUTURE – RATIONAL FUTURISTS




5. INDIGO lenses are for STEADY STATE FUTURE – EXTRAPOLATION and PATTERN ANALYSTS

6. VIOLET lenses are for DETERMINISITC FUTURE – STRATEGIC POSITIVISTS

THE ELTVILLE MODEL by Pero Mićić


• The Eltville Model serves as a holistic "cognitive map" for terms such as scenario,

vision, trend, wild card, assumption etc, - which may frequently be used in varying

context in different ways by diverse stakeholders. The terms used in the Eltville

Model are unambiguously defined and semantically related to each other - and are

further based on wide futures phenomenological analysis,.

– The ELTVILLE MODEL helps us all to structure our future scenarios and thoughts

about future outcomes to formulate future strategy in a coherent way without omitting

any important determining factors or neglecting any essential viewpoints.

– The ELTVILLE MODEL helps us to obtain some clarity on the most important Future

Management outcomes, goals and objectives and communicate in a clear narrative

about the future of our market and our companies place in that market.

– The ELTVILLE MODEL guides us to implement Strategy Analysis and Future

Management methods and tools in the areas where they are most effective.

• The Eltville Model is a result of observation and phenomenological analysis of more

than 800 workshops with management teams. It was developed by Pero Mićić and is

now being developed further by the Future Management Group consultants


• The SIX futures lenses and the resulting "ELTVILLE MODEL" bridges the gap between strategic management and corporate planning and futures studies - research for creating a better everyday way of life .

• Using phenomenon-based scenario planning and impact analysis, the ELTVILLE FUTURE MANAGEMENT! MODEL is proven in more than a thousand projects. Future Management Group have defined the essential meaning of Future Management terms and their key application to deliver a cognitive model and a cognitive map from them.

• The ELTVILLE MODEL helps us all to apply the common Strategy Analysis and Strategic Foresight tools much more effectively within a comprehensive Futures Framework. This model also provides participants with a road map for thinking and communicating about the future with your stakeholders and an integrated future-oriented structure for managing strategy delivery projects.


1. BLUE lenses are for PROBABLISTIC FUTURE – RATIONAL FUTURISTS 2. RED lenses are for FUTURE THREATS – DISRUPTIVE FUTURISTS 3. GREEN lenses are for FUTURE OPPORTUNISTIIES – EVOLUTIONARY FUTURISTS 4. GOLD lenses are for DESIRED FUTURE VISION – GOAL ANALYSTS 5. INDIGO lenses are for STEADY STATE FUTURE – EXTRAPOLATION and PATTERN ANALYSTS 6. VIOLET lenses are for DETERMINISITC FUTURE – STRATEGIC POSITIVISTS

• The Eltville Model of Future Management is used by companies and public institutions to

support thinking and communicating about future environmental changes, the early

recognition of future markets, the development of future strategies and the building up of

future competence with a sound system of terms. The Eltville Model provides a

comprehensive and integrated terminology. It links the requirements on scientific future

management with the necessities of a company’s day-to-day business.

• The ELTVILLE MODEL has been developed through futures research in more than a

thousand workshops and projects with governmental and non-profit organizations – as well

as with major corporations around the world, - including BOSCH, Microsoft, BAYER,

AstraZeneca, Roche, Ernst+Young, Ford, Vodafone, EADS and Nestle.


1. BLUE lenses are for PROBABLISTIC FUTURE – RATIONAL FUTURISTS




5. INDIGO lenses are for STEADY STATE FUTURE – EXTRAPOLATION / PATTERN ANALYSTS

6. VIOLET lenses are for DETERMINISITC FUTURE – STRATEGIC POSITIVISTS


The Eltville Model – Rational Futurism

1. The ELTVILLE MODEL BLUE lenses are for a PROBABLISTIC FUTURE – RATIONAL FUTURISM – Rational Futurists believe that the future is, to a large extent, both unknown and unknowable. Reality is non-liner – that is, chaotic – and therefore it is impossible to predict the future. With chaos comes the potential for disruption. Possible and Alternative Futures emerge from the interaction of chaos and uncertainty amongst the interplay of current trends and emerging factors of change – presenting an inexorable mixture of challenges and opportunities.

• Probable future outcomes and events may be synthesised and implied via an intuitive assimilation and cognitive filtering of Weak Signals, inexorable trends, random and chaotic actions and disruptive Wild Card and Black Swan events. Just as the future remains uncertain, indeterminate and unpredictable, so it will be volatile and enigmatic – but it may also be subject to synthesis by man.....

The Probabilistic Future – Synthesis: - – Rational Futurism

– Weak Signals and Wild Cards

– Complex Systems and Chaos Theory

– Cognitive Filtering and Intuitive Assimilation

– Nominal Group Conferences and Delphi Surveys

– Horizon Scanning, Tracking and Monitoring for emerging catalysts of Global Change

The Eltville Model – Disruptive Futurism

2. The ELTVILLE MODEL RED lenses are for FUTURE THREATS – DISRUPTIVE FUTURISM – Disruptive Futurism is an ongoing forward analysis of the impact of new and emerging factors of Disruptive Change on Environmental, Political, Economic, Social, Industrial, Agronomy and Technology and how Disruptive Change is driving Business and Technology Innovation. Understanding how current patterns, trends and extrapolations along with emerging agents and catalysts of change interact with chaos, disruption and uncertainty (Random Events) - to create novel opportunities – as well as posing clear and present dangers that threaten the status quo of the world as we know it today.....

• The purpose of the “Disruptive Futurist” role is to provide future analysis and strategic direction to support senior client stakeholders who are charged by their organisations with thinking about the future. This involves enabling clients to anticipate, prepare for and manage the future by helping them to understanding how the future might unfold - thus realising the Stakeholder Strategic Vision and Communications / Benefits Realisation Strategies. This is achieved by scoping, influencing and shaping client organisational change and driving technology innovation to enable rapid business transformation.

• Future Threats and Chaos – Disruptive Futurism -

– Risk Management – Disruptive Change – Weak Signals and Wild cards – Black Swan (Random) Events – Complex Systems and Chaos Theory – Horizon Scanning, Monitoring and Tracking for Weak Signals

The Eltville Model – Evolutionary Futurism

3. In the ELTVILLE MODEL GREEN lenses represent FUTURE OPPORTUNISTIIES – EVOLUTIONARY FUTURISM – Evolutionists believe that the geological, ecological and climatic systems interact with human activity to behave as a self-regulating collection of loosely coupled forces and systems – the Gaia Theory. Global Massive Change is driven by climatic, geological, biosphere, anthropologic and geo-political systems dominate at the macro-level – and at the micro-level local weather, ecology and environmental, social and economic sub-systems prevail.

4. The future will evolve from a series of actions and events which emerge, unfold and develop – and then plateau, decline and collapse. These actions and events are essentially natural responses to human impact on ecological and environmental support systems - creating massive global change through population growth, environmental degradation and scarcity of natural resources. Over the long term, global stability and sustainability of those systems will be preserved – at the expense of world-wide human population levels.

• The Creatable Future – Opportunities: - – Complex Adaptive Systems (CAS)

– Evolution - Opportunities and Adaptation

– Geological Cycles and Biological Systems

– Social Anthropology and Human Behaviour

– Global Massive Change and Human Impact

– Climatic Studies and Environmental Science

– Population Curves and Growth Limit Analysis

The Eltville Model – Goal Analysts

4. In the ELTVILLE MODEL GOLD lenses stand for our PREFERED and DESIRED FUTURE VISION – GOAL ANALYSTS believe that the future will be governed by the orchestrated vision, beliefs, goals and objectives of various influential and well connected Global Leaders, working with other stakeholders - movers, shakers and influencers such as the good and the great in Industry, Economics, Politics and Government, along with other well integrated and highly coordinated individuals from Academia, Media and Society in general – and realised through the plans and actions of global and influential organizations, institutions and groups to which they belong.

• The shape of the future may thus be discerned by Goal Analysis and interpretation of the policies, behaviours and actions of such individuals and of those groups to which they subscribe and belong.

The Preferred Future – Vision: -

– Goal Analysis

– Causal Layer Analysis (CLA)

– Value Models and Roadmaps

– Political Science and Policy Studies

– Religious Studies and Future Beliefs

– Peace and Conflict Studies, Military Science

– Leadership Studies and Stakeholder Analysis

The Eltville Model – Extrapolation Analysis

5. In the ELTVILLE MODEL – INDIGO lenses are for EXTRAPOLATION – PATTERN and TREND ANALYSIS. Extrapolation, Pattern and Trend Analysts believe that the past is the key to the future-present. The future-present is therefore just a logical extrapolation, extension and continuum of past events, carried foreword on historic waves, cycles, patterns and trends.....

• Throughout eternity, all that is of like form comes around again – everything that is the

same must return again in its own everlasting cycle.....

• Marcus Aurelius – Emperor of Rome •

• As the future-present develops and unfolds – it does so as a continuum of time past, time present and time future – and so eternally perpetuating the eternally unfolding, extension, replication and preservation of those historic cycles, patterns and trends that have shaped and influenced actions and events throughout time.

The Probable Future – Assumptions: -

– Patent and Content Analysis

– Causal Layer Analysis (CLA)

– Fisher-Pry and Gompertz Analysis

– Pattern Analysis and Extrapolation

– Technology and Precursor Trend Analysis

– Morphological Matrices and Analogy Analysis

The Eltville Model - Strategic Positivism

6. The ELTVILLE MODEL VIOLET lenses are for STRATEGIC POSITIVISM – STRATEGIC POSITIVISTS are deterministic, optimistic and somewhat Utopian in nature – they believe that their future outcomes, goals and objectives can be determined using Strategic Foresight and the future designed via Future Management – strategy planning, and delivery through the action link – to be delivered through Business Transformation – organisational change, process improvement and technology refreshment – so that their desired future becomes both realistic and achievable.

• The future may develop and unfold so as to comply with our positive vision of an ideal future – and thus fulfil all of our desired outcomes, goals and objectives – in order that the planned future becomes attainable and our preferred future options may ultimately be realised.

• The Planned Future – Strategy: -

– Linear Systems and Game Theory

– Scenario Planning and Impact Analysis

– Future Landscape Modelling and Terrain Mapping

– Threat Assessment and Risk Management

– Economic Modelling and Financial Analysis

– Strategic Foresight and Future Management


• Scenario Planning and Impact Analysis is the archetypical method for futures studies

because it embodies the central principles of the discipline:

– It is vitally important that we think deeply and creatively about the future, or else we run

the risk of being either unprepared or surprised – or both......

– At the same time, the future is uncertain - so we must prepare for a range of multiple

possible and plausible futures, not just the one we expect to happen.

• Scenarios contain the stories of these multiple futures, from the expected to the

wildcard, in forms that are analytically coherent and imaginatively engaging. A good

scenario grabs us by the collar and says, ‘‘Take a good look at this future. This could be

your future. Are you going to be ready?’’

• As consultants and organizations have come to recognize the value of scenarios, they

have also latched onto one scenario technique – a very good one in fact – as the

default for all their scenario work. That technique is the Royal Dutch Shell/Global

Business Network (GBN) matrix approach, created by Pierre Wack in the 1970s and

popularized by Schwartz (1991) in the Art of the Long View and Van der Heijden (1996)

in Scenarios: The Art of Strategic Conversations. In fact, Millett (2003, p. 18) calls it the

‘‘gold standard of corporate scenario generation.’’

Outsights "21 Drivers for the 21st Century"

1. War, terrorism and insecurity 2. Layers of power 3. Economic and financial stability 4. BRICs and emerging powers • Brazil • Russia • India • China

5. The Five Flows of Globalisation • Ideas • Goods • People • Capital • Services

6. Intellectual Property and Knowledge 7. Health, Wealth and Wellbeing 8. Demographics, Ethnographics and Social

Anthropology - Transhumanism 9. Population Drift, Migration and Mobility 10. Trust and Reputation 11. Human Values and Beliefs

12. History, Culture and Human Identity 13. Consumerism and the rise of the Middle

Classes 14. Networks and Social Connectivity 15. Space - the final frontier

• The Cosmology Revolution

16. Science and Technology Futures • The Nano Revolution • The Quantum Revolution • The Information Revolution • The Bio-Technology Revolution • The Energy Revolution • Oil Shale Kerogen • Tar

Sands • Methane Hydrate • Nuclear Fusion •

17. Science and Society - Social Impact of Technology

18. Natural Resources – availability, scarcity and control

19. Climate Change • Global Massive Change – the Climate Revolution

20. Environmental Degradation & Mass Extinction 21. Urbanisation


• Scenarios are specially constructed stories about the future - each one portraying

a distinct, challenging and plausible world in which we might one day live and work - and for which we need to anticipate, plan and prepare.

• The Outsights Technique emphasises collaborative scenario building with internal clients and stakeholders. Embedding a new way of thinking about the future in the organisation is essential if full value is to be achieved – a fundamental principle of the “enabling, not dictating” approach

• The Outsights Technique promotes the development and execution of purposeful action plans so that the valuable learning experience from “outside-in” scenario planning enables building profitable business change.

• The Outsights Technique develops scenarios at the geographical level; at the business segment, unit and product level, and for specific threats, risks and challenges facing organisations. Scenarios add value to organisations in many ways: - future management, business strategy, managing change, managing risk and communicating strategy initiatives throughout an organisation.

Seeing in Multiple Horizons: - Connecting Strategy to the Future

• THE THREE HORIZONS MODEL describes a Strategic Foresight method called “Seeing in Multiple Horizons: - Connecting Strategy to the Futures " The current THREE HORIZONS MODEL differs significantly from the original version first described in management literature over a decade ago. This model enables a range of Futures Studies techniques to be integrated with Strategy Analysis methods in order to reveal powerful and compelling future insights – and may be deployed in various combinations, whenever or wherever the Futures Studies techniques and Strategy Analysis methods are deemed to support the futures domains, subjects, applications and data in the current study.

• THE THREE HORIZONS MODEL method connects the Present Timeline with deterministic (desired or proposed) futures, and also helps us to identify probabilistic (forecast or predicted) future scenarios which may emerge as a result of interaction between embedded present-day factors and emerging catalysts of change – thus presenting us with a range of divergent possible futures. The “Three Horizons” method connects to models of change developed within the “Social Shaping” Strategy Development Framework via the Action Link to Strategy Execution. Finally, it summarises a number of futures applications where this evolving technique has been successfully deployed.

• The new approach to “Seeing in Multiple Horizons: - Connecting Strategy to the Future” has several unique features. It can relate change drivers and trends-based futures analysis to emerging issues. It enables policy or strategy implications of futures to be identified – and links futures work to processes of change. In doing so this enables Foresight to be connected to existing and proposed underlying system domains and data structures, with different rates of change propagation impacting across different parts of the system, and also to integrate seamlessly with tools and processes which facilitate Strategic Analysis. This approach is especially helpful where there are complex transformations which are likely to be radically disruptive in nature - rather than simple incremental transitions.

Andrew Curry

Henley Centre HeadlightVision

United Kingdom

Anthony Hodgson

Decision Integrity

United Kingdom


The Three Horizons


• The way that we think about the future must mirror how the future actually unfolds. As we have learned from recent experience, the future is not a straightforward extrapolation of simple, single-domain trends. We now have to consider ways in which the possibility of random, chaotic and radically disruptive events may be factored into enterprise threat assessment and risk management frameworks and incorporated into enterprise decision-making structures and processes.



Futures Studies • Precognition and Prediction – Contemplative, mystic, meditative and even

psychic methods for pre-cognitive viewing of the future and how the future might unfold. These future scanning activities have been recorded throughout history (Flavius Josephus, Michel Nostradamus, Leonardo da Vinci, Jules Verne, H.G. Wells) and are also well known within certain cultures (Central American Indians) and government agencies (US and Soviet Military) – some techniques may also involve the use of hypnotic or hallucinogenic states.

• Futures Studies, Foresight, or Futurology – is the science, practice and art of postulating possible, probable, and preferable futures. Futures studies (colloquially called "Futures" by many of the field's practitioners) seeks to understand what is likely to continue, what is likely to change, and what is a novel, emerging pattern or trend. Part of the discipline thus seeks a systematic and extrapolation-based understanding of both past and present events - in order to determine the probability and impact of future events, patterns and trends.

• Future Management– is an interdisciplinary curriculum, studying yesterday's and today's changes, and aggregating and analyzing both lay and professional content and strategies, beliefs and opinions, forecasts and predictions with respect to shaping tomorrow. It includes analysing the sources, agents and causes, patterns and trends of both change and stability in an attempt to develop foresight and to map possible, probable and alternative futures.

Foresight • Foresight draws on traditions of work in long-range forecasting and strategic

planning horizontal policymaking and democratic planning, horizon scanning and futures studies (Aguillar-Milan, Ansoff, Feather, van der Hijden, Slaughter et all) - but was also highly influenced by systemic approaches to innovation studies, global design, massive change, science and technology futures, economic, social and demographic policy, fashion and design - and the analysis of "weak signals" and "wild cards", "future trends“ "critical technologies“ and “cultural evolution".

– The longer-term - futures that are usually at least 10 years away (though there are some exceptions to this, especially in its use in private business). Since Foresight is an action-oriented discipline (via the planning link) it will rarely be applied to perspectives beyond a few decades out. Where major infrastructure decisions such as petrology reservoir exploitation, aircraft design, power station construction, transport hubs and town master planning decisions are concerned - then the planning horizon may well be half a century.

– Alternative futures: it is helpful to examine alternative paths of development, not just what is currently believed to be most likely or business as usual. Often Foresight will construct multiple scenarios. These may be an interim step on the way to creating what may be known as positive visions, success scenarios or aspirational futures. Sometimes alternative scenarios will be a major part of the output of a Foresight study, with the decision about what preferred future to build being left to other mechanisms (Planning and Strategy).

Strategic Foresight • Strategic Foresight is the ability to create and maintain a high-quality, coherent

and functional forward view, and to use the insights arising in useful organisational ways. For example to detect adverse conditions, guide policy, shape strategy, and to explore new markets, products and services. It represents a fusion of futures methods with those of strategic management (Slaughter (1999), p.287).

– Probabilistic Futures (Rational Futurism) – assessing possible, probable and alternative futures – selecting those futures offering conditions that best fit our strategic goals and objectives for achieving a preferred and desired future. Filtering for a more detailed analysis may be achieved by discounting isolated outliers and focusing upon those closely clustered future descriptions which best support our desired future outcomes, goals and objectives.

• Strategy Visioning – Future outcomes, goals and objectives are defined via Strategic Foresight and are determined by design, planning and management - so that the future becomes realistic and achievable. Possible futures may comply with our preferred options - and therefore our vision of an ideal future and desired outcomes could thus be fulfilled.

– Deterministic Futurism (Strategic Positivism) – articulating a single, preferred vision of the future. The future will conform to our preferred options - thus our vision of an ideal future and desired outcomes will be fulfilled.


• ECONOMIC MODELLING and LONG-RANGE FORECASTING •

• Economic Modelling and Long-range Forecasting is driven by atomic Data Warehouse

Structures and sophisticated Economic Models containing both Historic (up to 200 years daily

closing prices for Commodities, shares and bonds) and Future values (daily forecast and weekly

projected price curves, monthly and quarterly movement predictions, and so on for up to 50

years into the future – giving a total timeline of up to 250 years (Historic + 50 years Future trends

summary, outline movements and highlights). Forecast results are obtained using Economic

Models - Quantitative (technical) Analysis (Monte Carlo Simulation, Pattern and Trend Analysis -

Economic Growth and Recession / Depression shapes and Commodity Price Data Sets) in order

to construct a continuous 100 year “window” into Commodity Price Curves and Business Cycles

for Cluster Analysis and Causal Layer Analysis (CLA) – which in turn is used for driving out

Qualitative (narrative) Scenario Planning and Impact Analysis for describing future narrative epic

stories, scenarios and use-cases.

• PREDICTIVE ANALYITICS and EVENT FORECASTING •

• Predictive Analytics and Event Forecasting uses Horizon Scanning, Tracking and Monitoring

methods combined with Cycle, Pattern and Trend Analysis techniques for Event Forecasting and

Propensity Models in order to anticipate a wide range of business. economic, social and political

Future Events – ranging from micro-economic Market phenomena such as forecasting Market

Sentiment and Price Curve movements - to large-scale macro-economic Fiscal phenomena

using Weak Signal processing to predict future Wild Card and Black Swan Events - such as

Monetary System shocks.


• MARKET RISK •

Market Risk = Market Sentiment – Actual Results (Reality)

• The two Mood States – “Greed and Fear” are primitive human instincts which, until now, we've

struggled to accurately qualify and quantify. Social Networks, such as Twitter and Facebook,

burst on to the scene five years ago and have since grown into internet giants. Facebook has

over 900 million active members and Twitter over 250 million, with users posting over 2 billion

"tweets“ or messages every week. This provides hugely valuable and rich insights into how

Market Sentiment and Market Risk are impacting on Share Support / Resistance Price Levels –

and so is also a source of real-time data that can be “mined” by super-fast computers to forecast

changes to Commodity Price Curves

• STRATEGIC FORESIGHT •

• Strategic Foresight is the ability to create and maintain a high-quality, coherent and functional

forward view, and to utilise Future Insights in order to gain Competitive Advantage - for example

to identify and understand emerging opportunities and threats, to manage risk, to inform

planning and forecasting and to shape strategy development. Strategic Foresight is a fusion of

Foresight techniques with Strategy Analysis methods – and so is of great value in detecting

adverse conditions, threat assessment, guiding policy and strategic decision-modelling, in

identifying and exploring novel opportunities presented by emerging technologies, in evaluating

new markets, products and services and in driving transformation and change.


• INNOVATION •

• Technology Innovation is simply combining existing resources in new and different ways –

in order to create novel and innovative Products and Services. Understanding the impact

of Technology Convergence is the Key to driving Innovation. Many common and familiar

objects in use today exist only as a result of technology convergence - your average,

everyday passenger vehicle or laptop computer is the culmination of a series of technology

consolidation and integration events of a large number of apparently separate, unrelated

technological innovations and advancements. Light-weight batteries were developed to

provide independence from fixed power sockets and hard-disk drives were made compact

enough to be installed in portable devices. Then the smart phone and tablet resulted from

a further convergence of technologies such as cellular telecommunications, mobile

internet, and Smart Apps - mini-applications that do not need an on-board hard-disk drive.

• FUTURE MANAGEMENT •

• Providing future analysis and strategic advice to stakeholders so that they might

understanding how the Future may unfold - in order to anticipate, prepare for and manage

the Future, to resolve challenging business problems, to envision, architect, design and

deliver novel solutions in support of major technology refreshment and business

transformation programmes • Future Analysis • Innovation • Strategic Planning •

Business Transformation • Technology Refreshment •


. • GEO-DEMOGRAPHICS •

• The profiling and analysis of large aggregated datasets in order to determine a ‘natural’ or

implicit structure of data relationships or groupings where no prior assumptions are made

concerning the number or type of groups discovered or group relationships, hierarchies or

internal data structures - in order to discover hidden data relationships - is an important starting

point forming the basis of many statistical and analytic applications. The subsequent explicit

Cluster Analysis as of discovered data relationships is a critical technique which attempts to

explain the nature, cause and effect of those implicit profile similarities or geographic

distributions. Geo-demographic techniques are frequently used in order to profile and segment

populations by ‘natural’ groupings - such as common behavioural traits, Clinical Trial, Morbidity

or Actuarial outcomes, along with many other shared characteristics and common factors –and

then attempt to understand and explain those natural group affinities and geographical

distributions using methods such as Causal Layer Analysis (CLA).....

• Social Media is the fastest growing category of user-provided global content and will eventually

grow to 20% of all internet content. Gartner defines social media content as unstructured data

created, edited and published by users on external platforms including Facebook, MySpace,

LinkedIn, Twitter, Xing, YouTube and a myriad of other social networking platforms - in addition

to internal Corporate Wikis, special interest group blogs, communications and collaboration

platforms. Social Mapping is the method used to describe how social linkage between

individuals defines Social Networks and to understand the nature and dynamics of intimate

relationships between individuals


• GIS MAPPING and SPATIAL DATA ANALYSIS •

• A Geographic Information System (GIS) integrates hardware, software, and data capture devices for acquiring, managing, analysing, distributing and displaying all forms of geographically dependant location data – including machine generated data such as Computer-aided Design (CAD) data from land and building surveys, Global Positioning System (GPS) terrestrial location data - as well as all kinds of aerial and satellite image data.

• Spatial Data Analysis is a set of techniques for analysing spatial (Geographic) location data. The results of spatial analysis are dependent on the locations of the objects being analysed. Software that implements spatial analysis techniques requires access to both the locations of objects and their physical attributes. Spatial statistics extends traditional statistics to support the analysis of geographic data. Spatial Data Analysis provides techniques to describe the distribution of data in the geographic space (descriptive spatial statistics), analyse the spatial patterns of the data (spatial pattern or cluster analysis), identify and measure spatial relationships (spatial regression), and create a surface from sampled data (spatial interpolation, usually categorized as geo-statistics).


• “BIG DATA” •

• “Big Data” refers to vast aggregations (super sets) of individual datasets whose size and

scope is beyond the capability of conventional transactional Database Management

Systems and Enterprise Software Tools to capture, store, analyse and manage. Examples

of Big Data include the vast and ever changing amounts of data generated in social

networks where we have (unstructured) conversations with each other, news data streams,





• Data Set Mashing and “Big Data” Global Content Analysis – supports Horizon Scanning,

Monitoring and Tracking activities by taking numerous, apparently un-related RSS and

other Information Streams and Data Feeds, loading them into Very large Scale (VLS) DWH

Structures and Document Management Systems for Real-time Analytics – searching for

and identifying possible signs of relationships hidden in data (Facts/Events)– in order to

discover and interpret previously unknown “Weak Signals” indicating emerging and

developing Application Scenarios, Patterns and Trends - in turn predicating possible,

probable and alternative global transformations unfolding as future “Wild Card” or “Black

Swan” events.


• WAVE-FORM ANAYITICS in “BIG DATA” •

• Wave-form Analytics help identify Cycles, Patterns and Trends in Big Data – characterised as

a sequence of high and low activity in time-series data – resulting in periodic increased and

reduced phases in regular, recurring cyclic trends. This approach supports an integrated study

of the impact of multiple concurrent cycles - and no longer requires iterative and repetitive

processes of trend estimation and elimination from the background “noise”.

• FORENSIC “BIG DATA” •

• Social Media Content and Spatial Mapping Data is used in order to understand intimate

personal relationships between individuals and to identify, locate and describe their participation

in various Global Social Networks. Thus the identification, composition, monitoring, tracking

,activity and traffic analysis of Social Networks Criminal Enterprises and Terrorist Cells – as

defined by common locations, business connections, social links and inter-personal

relationships – is used by Businesses to drive Influencer Programmes and by Government for

National Security, Counter-Terrorism, Anti-Trafficking, Criminal Investigation and Fraud

Prevention purposes.....

• Forensic “Big Data” combines the use of Social Media and Social Mapping Data in order to

understand intimate inter-personal relationships for the purpose of National Security, anti-

Trafficking and Fraud Prevention – through the identification, composition, activity analysis and

monitoring of Criminal Enterprises and Terrorist Cells.....

Thinking about the Future Framework


Professors Peter Bishop and Andy Hines at the University of Texas Futures Studies School at the

Houston Clear Lake site have developed a definitive Strategic Management Framework –

Thinking About the Future

Thinking about the Future Professors Peter Bishop and Andy Hines at the University of Texas Futures

Studies School at the Houston Clear Lake site have developed a definitive Strategic Foresight Framework –


1. FRAMING AND SCOPING •

• This important first step enables organizations to define the purpose. focus, scope and boundaries of the Political, Legal, Economic, Cultural, Business and Technology problem / opportunity domains requiring resolution. Taking time at the outset of a project, the Strategic Foresight Team defines the Futures Study domain, outlines the required outcomes, goals and objectives and determines how best to achieve them. •

• Strategy Study Definition – Problem / Opportunity Domains: -

– Definition - Focus, Scope, Purpose and Boundaries

– Approach - What – How – Why – Who – When – Where?

– Justification - Cost, Duration and Resources v. Future Benefits and Cash Flows

Thinking about the Future 2. ENGAGING •

• This second phase is about stakeholder management - developing action agendas for mobilising the Programme and opening stakeholders communications channels, soliciting collaborative participation and input.

• This may involve staging a wide range of Programme kick-off Events , organising Stakeholder Strategy Communications, Target-setting and Action Planning, establishing mechanisms for reporting actual achievement against targets – in order that the Strategic Foresight Team engage a wide range of stakeholders, presents a future-oriented, customer-focussed approach and enables the efficient delivery of Strategy Study artefacts & benefits in planned / managed work streams. •

• Strategy Study Mobilisation – Stakeholder Engagement: -

– Communication Strategy

– Benefits Realisation Strategy

– Strategy Study Programme Plan

– Stakeholder, SME and TDA Strategy Study Launch Events

Thinking about the Future 3. RESEARCH – HORIZON SCANNING, MONITORING AND TRACKING: •

• Once the Strategic Foresight Team is clear about the engagement boundaries, purpose, problem / opportunity domains and scope of a Strategy Study - they can begin to scan both internal and external environments for all relevant input content – information and data describing extrapolations, patterns and trends – or indicating global transformations, emerging and developing factors and catalysts of change – and to search for, seek out and identify any Weak Signals indicating the potential for disruptive Wild Card or Black Swan events. •

• Strategy Investigation – Content Capture: - – Factors and Catalysts of Change

– Extrapolations, Patterns and Trends

– Internal and External Content, Information and Data

– Horizon Scanning, Monitoring and Tracking Systems and Infrastructure

Thinking about the Future 4. STRATEGY DISCOVERY – STAKEHOLDER EVENTS & STRATEGY THEMES •

• Here we begin to identify and extract useful information from the mass of Research Content that we have collected. Critical Success Factors, Strategy Themes and Value Propositions begin to emerge from Data Set “mashing”, Data Mining and Analytics against the massed Research Data – and all supplemented via the very human process of Cognitive Filtering and Intuitive Assimilation of selected information - through Discovery Workshops, Strategy Theme Forums, Value Chain Seminars, Special Interest Group Events and one-to-one Key Stakeholder Interviews. •

• Strategy Discovery – Content Analysis: -

– Data Set “mashing”, Data Mining and Analytics

– Stakeholder, SME and TDA Strategy Discovery Events

– Discovered Assumptions, Critical Success Factors, Strategy Themes and Value Propositions


5. STRATEGIC RISK MANAGEMENT •

• The underlying premise of Strategic Risk Management is that every enterprise exists to provide value for its stakeholders. All entities face uncertainty and the possibility of chaos and disruption. Risk Management is the evaluation of uncertainty. The challenge is to determine how much risk we are able to accept as we strive to grow stakeholder value. Uncertainty presents both opportunity and risk with the possibility of either erosion or enhancement of value. Strategic Foresight enables stakeholders to deal effectively with uncertainty and associated risk and opportunity - thus enhancing the capability of the Enterprise to build long-term value. •

• Risk Management – Value Chain Building: -

– Risk Research and Identification – Uncertainty, Chaos and Disruption – Identified Assumptions, Critical Success Factors, Strategy Themes and Value

Propositions

Strategic Risk Management

• Systemic Risk (external threats) – Political Risk – Political Science, Futures Studies and Strategic Foresight – Economic Risk – Fiscal Policy, Economic Analysis, Modelling and Forecasting – Wild Card Events – Horizon Scanning, Tracking and Monitoring – Weak Signals – Black Swan Events – Scenario Planning & Impact Analysis – Future Management

• Market Risk (macro-economic threats) – Equity Risk – Traded Instrument Product Analysis and Financial Management – Currency Risk – FX Curves and Forecasting – Commodity Risk – Price Curves and Forecasting – Interest Rate Risk – Interest Rate Curves and Forecasting

• Trade Risk (micro-economic threats)

– Credit Risk – Debtor Analysis and Management – Liquidity Risk – Solvency Analysis and Management – Insurance Risk – Underwriting Due Diligence and Compliance – Counter-Party Risk – Counter-Party Analysis and Management


• Operational Risk (internal threats)

– Legal Risk – Contractual Due Diligence and Compliance – Statutory Risk – Legislative Due Diligence and Compliance – Regulatory Risk – Regulatory Due Diligence and Compliance – Competitor Risk – Competitor Analysis, Defection Detection / Churn Management – Reputational Risk – Internet Content Scanning, Intervention / Threat Management – Corporate Responsibility – Enterprise Governance, Reporting and Controls – Digital Communications and Technology Risk

• Security Risk – Security Principles, Policies and Architecture • Process Risk – Business Strategy and Architecture • Information Risk – Information Strategy and Architecture • Technology Risk – Technology Strategy and Architecture • Stakeholder Risk – Benefits Realisation Strategy and Communications Management • Vendor / 3rd Party Risk – Strategic Vendor Analysis and Supply Chain Management

Thinking about the Future 6. THREAT ANALYSIS •

• In most organizations, many stakeholders, if unchallenged, tend to believe that threat

scenarios - as discovered in various SWOT / PEST Analyses - are going to play out pretty much the same way as they have always done so in the past. When the Strategic Foresight Team probes an organization’s view of the future, they usually discover an array of unexamined, unexplained assumptions tending to either maintain the current status quo – or converging around discrete clusters of small, linear, incremental future changes •

• Threat Analysis – Value Chain Analysis: - – Threat Analysis, Assessment and Prioritisation

– Global Transformations, Factors and Catalysts of Change

– Analysed Assumptions, Critical Success Factors, Strategy Themes and Value Propositions

Risk Management

• Risk Management is a structured approach to managing uncertainty through foresight and planning. A risk is related to a specific threat (or group of related threats) managed through a sequence of activities using various resources: -

Risk Research – Risk Identification – Scenario Planning & Impact Analysis – Risk Assessment – Risk Prioritization – Risk Management Strategies – Risk Planning –

Risk Mitigation

• Risk Management strategies may include: - – Transferring the risk to another party

– Avoiding the risk

– Reducing the negative effect of the risk

– Accepting part or all of the consequences of a particular risk .

• For any given set of Risk Management Scenarios, a prioritization process ranks those risks with the greatest potential loss and the greatest probability of occurrence to be handled first – and those risks with a lower probability of occurrence and lower consequential losses are then handled subsequently in descending order of impact. In practice this prioritization can be challenging. Comparing and balancing the overall threat of risks with a high probability of occurrence but lower loss -versus risks with higher potential loss but lower probability of occurrence -can often be misleading.

Risk Management • Scenario Panning and Impact Analysis: - In any Opportunity / Threat Assessment

Scenario, a prioritization process ranks those risks with the greatest potential loss and the greatest probability of occurring to be handled first - subsequent risks with lower probability of occurrence and lower consequential losses are then handled in descending order. As a foresight concept, Wild Card or Black Swan events refer to those events which have a low probability of occurrence - but an inordinately high impact when they do occur.

– Risk Assessment and Horizon Scanning have become key tools in policy making and strategic planning for many governments and global enterprises. We are now moving into a period of time impacted by unprecedented and accelerating transformation by rapidly evolving catalysts and agents of change in a world of increasingly uncertain, complex and interwoven global events.

– Scenario Planning and Impact Analysis have served us well as a strategic planning tools for the last 15 years or so - but there are also limitations to this technique in this period of unprecedented complexity and change. In support of Scenario Planning and Impact Analysis new approaches have to be explored and integrated into our risk management and strategic planning processes.

• Back-casting and Back-sight: - “Wild Card” or “Black Swan” events are ultra-extreme manifestations with a very low probability of, occurrence - but an inordinately high impact when they do occur. In any post-apocalyptic “Black Swan Event” Scenario Analysis, we can use Causal Layer Analysis (CLA) techniques in order to analyse and review our Risk Management Strategies – with a view to identifying those Weak Signals which may have predicated subsequent appearances of unexpected Wild Card or Black Swan events.

Thinking about the Future 7. STRATEGIC FORESIGHT •

• The prime activity in the Strategic Foresight Process is, therefore, to challenge the

status quo viewpoint and provoke the organisation into thinking seriously about the possibility that things may not continue as they always have done - and in fact, rarely do so.

• Strategic Foresight processes should therefore include searching for and identifying any potential Weak Signals predicating future Wild Card and Black Swan events – in doing so, revealing previously hidden factors and catalysts of change – thus exposing a much wider range of challenges, issues, problems, threats, opportunities and risks than may previously have been considered. •

• Strategic Foresight – Value Chain Management: - – Risk Planning, Mitigation and Management

– Weak Signals, Wild Cards and Black Swan Events

– Managed Assumptions, Critical Success Factors, Strategy Themes and Value Propositions

Thinking about the Future 8. SCENARIO FORECASTING •

• Scenarios are stories about how the future may unfold – and how that future will

impact on the way that we work and do business with our business partners, customers and suppliers. The Strategy Study considers a broad spectrum of possible scenarios as the only sure-fire way to develop robust strategic responses that will securely position the Strategic Foresight Programme to deal with every opportunity and threat domain that may transpire.

• The discovery of multiple scenarios and their associated opportunity / threat impact assessments, along with their probability of materialising – covers a wide range of possible and probable Opportunity / Threat situations – describing a rich variety of POSSIBLE, PROBABLE and ALTERNATIVE FUTURES •

• Scenario Forecasting – Impact Analysis: - – Possible, Probable and Alternative Future Scenarios

– Clustered Assumptions, Critical Success Factors, Strategy Themes

– Possible Future Business Models and Value Propositions, Products and Services

Thinking about the Future 9. STRATEGY VISIONING, FORMULATION AND DEVELOPMENT •

• After forecasting has laid out a range of potential Future Scenarios, visioning comes

into play — generating a pragmatic view of our “preferred” Future Environment – thus starting to suggest stretch goals for moving towards our “ideal” Strategy Models - using the Strategic Principles and Policies to drive out the “desired” Vision, Missions, Outcomes, Goals and Objectives •

• Strategy Visioning, Formulation and Development: -

– Strategic Principles and Policies, Guidelines and Best Practices

– Strategy Models and desired Vision, Missions, Outcomes, Goals and Objectives

– Proposed Future Business Models and Value Propositions, Products and Services

Thinking about the Future 10. PLANNING: the bridge between the VISION and the ACTION – the “ACTION LINK” •

• Here, the Strategy team transforms the desired Vision, Missions, Outcomes, Goals

and Objectives into the Strategic Master Plan, Enterprise Landscape Models, Strategic Roadmaps and Transition Plans for organisational readiness and mobilisation – maintaining Strategic Foresight mechanisms (Horizon Scanning, Monitoring and Tracking) to preserve the capability to quickly respond to fluctuations in internal and external environments •

• Strategy Enablement and Delivery Planning: - – Horizon Scanning, Monitoring and Tracking Systems and Infrastructure

– Planned Future Business Models and Value Propositions, Products and Services

– Strategic Master Plan, Enterprise Landscape Models, Roadmaps, Transition Plans

Thinking about the Future 11. ACTING •

• This penultimate phase is about communicating results and developing action agendas for mobilising strategy delivery – thus launching Business Programmes that will drive forwards to the realisation of Strategic Master Plans and Future Business Models through Business Transformation, Enterprise Portfolio Management, Technology Refreshment and Service Management - via Cultural Change, innovative multi-tier and collaborative Business Operating Models, Emerging Technologies (Smart Devices, the Smart Grid and Cloud Services) Business Process Re-engineering and Process Outsource - Onshore / Offshore. •

• Strategy Enablement and Delivery Programmes: - – Launched Future Business Models and Value Propositions, Products and Services

– Enterprise Portfolio Management - Technology Refreshment • System Management •

– Business Transformation – Organisational Re-structuring • Cultural Change • Business Process Management • Operating Models • Programme Planning & Control

– DCT Models - Demand / Supply Models • Shared Services.• Business Process Outsource •

– Emerging Technologies – Real-time Analytics • Smart Devices • Smart Grid • Mobile Computing • Cloud Services •

– Service Management - Service Access • Service Brokering • Service Provisioning • Service Delivery •

Thinking about the Future 12. REVIEWING •

• In this final phase, we focus on Key Lessons Learned and maintaining the flow of useful information from the Strategic Foresight mechanisms and infrastructure – in order to support an ongoing lean and agile capability to continually and successfully respond to the volatile and dynamic internal and external environment - through Futures Studies, Strategy Reviews, Business Planning and long-range Forecasting. •

We also prepare for the next round of the Strategy Cycle, beginning again with Phase 1 – Framing and Scoping.

• Strategy Review: - – Reviewed Business Models and Value Propositions, Products and Services

– Horizon Scanning, Monitoring and Tracking Systems, Infrastructure and Data

– Futures Studies, Strategy Reviews, Business Planning and long-range Forecasting

Peter Bishop and Andy Hines – University of Houston


13.The Crystal Ball Report

The Crystal Ball Report is a comprehensive document that aggregates the results from all of

the phases of strategic analysis. The findings from the technical analysis of SWAT, PEST and

5 Forces elements – along with an assessment of Business and Technical (non-functional)

Drivers / Requirements – taking into account your desired outcomes, goals and objectives.

Recommendations for Strategy Implementation – Organisational Change and Business

Transformation – is contained in the Strategic Roadmap are grouped together in The Crystal

Ball Report. SWAT, PEST and 5 Forces elements are highlighted. Stakeholder Groups, roles

and responsibilities are defined, a Strategy Programme Plan is generated and an Architecture

Roadmap is produced and elaborated. The Crystal Ball Report includes a detailed System

Dependency Map – outlining application system and platform candidates for Technology

Refreshment – COTS integration, Application Consolidation, Application Re-hosting in the

Cloud – or complete Application Renovation and Renewal based on new Enterprise Platforms.

The Crystal Ball Report is designed to become the “shared vision” reference point, where all

stakeholders can see how their needs and functions are both addressed and add value to the

overall corporate plan, keeping everyone “in the boat, and rowing in the same direction.”

Future Management Methods and Techniques

Many Economists and Economic Planners have arrived at the conclusion that most organizations have not yet widely developed sophisticated Economic Modelling

systems and integrated their outputs into the strategic planning process.

The objective of this paper is to shed some light into the practical state of business and economic environmental

scanning, tracking, monitoring and forecasting function in organizations Impacted by Business Cycles.


Professors Peter Bishop and Andy Hines at the University of Texas Futures Studies School at the

Houston Clear Lake site have developed a definitive Strategic Management Framework –



Professors Peter Bishop and Andy Hines at the University of Texas Futures Studies School at the Houston Clear Lake site have developed a definitive

Strategic Foresight Framework - Thinking About the Future

1. FRAMING AND SCOPING • This important first step enables organizations to define the purpose. focus, scope and boundaries of the Political, Legal, Economic, Cultural, Business and Technology problem / opportunity domains requiring resolution. Taking time at the outset of a project, the Strategic Foresight Team defines the Futures Study domain, outlines the required outcomes, goals and objectives and determines how best to achieve them. •

• Strategy Study Definition – Problem / Opportunity Domains: - – Definition - Focus, Scope, Purpose and Boundaries – Approach - What – How – Why – Who – When – Where? – Justification - Cost, Duration and Resources v. Future Benefits and Cash Flows


2. ENGAGING • This second phase is about stakeholder management - developing action agendas for mobilising stakeholders and opening communications channels, soliciting collaborative participation and input. This may involve staging a wide range of Stakeholder Events , organising Strategy Communications, Target-setting and Action Planning, establishing mechanisms for reporting actual achievement against targets – in order that the Strategic Foresight Team engage a wide range of stakeholders, presents a future-oriented, customer-focussed approach and enables the efficient delivery of Strategy Study artefacts & benefits in planned / managed work streams. •

• Strategy Study Mobilisation – Stakeholder Engagement: - – Communication Strategy – Benefits Realisation Strategy – Strategy Study Programme Plan – Stakeholder, SME and TDA Strategy Study Launch Events


3. RESEARCH – HORIZON SCANNING, MONITORING AND TRACKING: • Once the Strategic Foresight Team is clear about the engagement boundaries, purpose, problem / opportunity domains and scope of a Strategy Study - they can begin to scan both internal and external environments for all relevant input content – information and data describing extrapolations, patterns and trends – or indicating global transformations, emerging and developing factors and catalysts of change – and to search for, seek out and identify any Weak Signals indicating the potential for disruptive Wild Card or Black Swan events. •

• Strategy Investigation – Content Capture: - – Factors and Catalysts of Change – Extrapolations, Patterns and Trends – Internal and External Content, Information and Data – Horizon Scanning, Monitoring and Tracking Systems amd Infrastructure


4. STRATEGY DISCOVERY – STAKEHOLDER EVENTS & STRATEGY THEMES • Here we begin to identify and extract useful information from the mass of Research Content that we have collected. Critical Success Factors, Strategy Themes and Value Propositions begin to emerge from Data Set “mashing”, Data Mining and Analytics against the massed Research Data – and all supplemented via the very human process of Cognitive Filtering and Intuitive Assimilation of selected information - through Discovery Workshops, Strategy Theme Forums, Value Chain Seminars, Special Interest Group Events and one-to-one Key Stakeholder Interviews. •

• Strategy Discovery – Content Analysis: - – Data Set “mashing”, Data Mining and Analytics – Stakeholder, SME and TDA Strategy Discovery Events – Discovered Assumptions, Critical Success Factors, Strategy Themes and Value

Propositions


5. STRATEGIC RISK MANAGEMENT • The underlying premise of Strategic Risk Management is that every enterprise exists to provide value for its stakeholders. All entities face uncertainty and the possibility of chaos and disruption. Risk Management is the evaluation of uncertainty. The challenge is to determine how much risk we are able to accept as we strive to grow stakeholder value. Uncertainty presents both opportunity and risk with the possibility of either erosion or enhancement of value. Strategic Foresight enables stakeholders to deal effectively with uncertainty and associated risk and opportunity - thus enhancing the capability of the Enterprise to build long-term value. •

• Risk Management – Value Chain Building: - – Risk Research and Identification – Uncertainty, Chaos and Disruption – Identified Assumptions, Critical Success Factors, Strategy Themes and Value

Propositions


• Systemic Risk (external threats) – Political Risk – Political Science, Futures Studies and Strategic Foresight – Economic Risk – Fiscal Policy, Economic Analysis, Modelling and Forecasting – Wild Card Events – Horizon Scanning, Tracking and Monitoring – Weak Signals – Black Swan Events – Scenario Planning & Impact Analysis – Future Management

• Market Risk (macro-economic threats) – Equity Risk – Traded Instrument Product Analysis and Financial Management – Currency Risk – FX Curves and Forecasting – Commodity Risk – Price Curves and Forecasting – Interest Rate Risk – Interest Rate Curves and Forecasting

• Trade Risk (micro-economic threats)

– Credit Risk – Debtor Analysis and Management – Liquidity Risk – Solvency Analysis and Management – Insurance Risk – Underwriting Due Diligence and Compliance – Counter-Party Risk – Counter-Party Analysis and Management


• Operational Risk (internal threats)

– Legal Risk – Contractual Due Diligence and Compliance – Statutory Risk – Legislative Due Diligence and Compliance – Regulatory Risk – Regulatory Due Diligence and Compliance – Competitor Risk – Competitor Analysis, Defection Detection / Churn

Management – Reputational Risk – Internet Content Scanning, Intervention / Threat

Management – Corporate Responsibility – Enterprise Governance, Reporting and Controls – Digital Communications and Technology Risk

• Security Risk – Security Principles, Policies and Architecture • Process Risk – Business Strategy and Architecture • Information Risk – Information Strategy and Architecture • Technology Risk – Technology Strategy and Architecture • Stakeholder Risk – Benefits Realisation Strategy and Communications Management • Vendor / 3rd Party Risk – Strategic Vendor Analysis and Supply Chain Management


6. THREAT ANALYSIS • In most organizations, many stakeholders, if unchallenged, tend to believe that threat scenarios - as discovered in various SWOT / PEST Analyses - are going to play out pretty much the same way as they have always done so in the past. When the Strategic Foresight Team probes an organization’s view of the future, they usually discover an array of unexamined, unexplained assumptions tending to either maintain the current status quo – or converging around discrete clusters of small, linear, incremental future changes •

• Threat Analysis – Value Chain Analysis: - – Threat Analysis, Assessment and Prioritisation – Global Transformations, Factors and Catalysts of Change – Analysed Assumptions, Critical Success Factors, Strategy Themes and Value

Propositions


7. STRATEGIC FORESIGHT • The prime activity in the Strategic Foresight Process is, therefore, to challenge the status quo viewpoint and provoke the organisation into thinking seriously about the possibility that things may not continue as they always have done - and in fact, rarely do so. Strategic Foresight processes should therefore include searching for and identifying any potential Weak Signals predicating future Wild Card and Black Swan events – in doing so, revealing previously hidden factors and catalysts of change – thus exposing a much wider range of challenges, issues, problems, threats, opportunities and risks than may previously have been considered. •

• Strategic Foresight – Value Chain Management: - – Risk Planning, Mitigation and Management – Weak Signals, Wild Cards and Black Swan Events – Managed Assumptions, Critical Success Factors, Strategy Themes and Value

Propositions


8. SCENARIO FORECASTING • Scenarios are stories about how the future may unfold – and how that future will impact on the way that we work and do business with our business partners, customers and suppliers. The Strategy Study considers a broad spectrum of possible scenarios as the only sure-fire way to develop robust strategic responses that will securely position the Strategic Foresight Programme to deal with every opportunity and threat domain that may transpire. The discovery of multiple scenarios and their associated opportunity / threat impact assessments, along with their probability of materialising – covers a wide range of possible and probable Opportunity / Threat situations – describing a rich variety of POSSIBLE, PROBABLE and ALTERNATIVE FUTURES •

• Scenario Forecasting – Impact Analysis: - – Possible, Probable and Alternative Future Scenarios – Clustered Assumptions, Critical Success Factors, Strategy Themes – Possible Future Business Models and Value Propositions, Products and Services


9. STRATEGY VISIONING, FORMULATION AND DEVELOPMENT • After forecasting has laid out a range of potential Future Scenarios, visioning comes into play — generating a pragmatic view of our “preferred” Future Environment – thus starting to suggest stretch goals for moving towards our “ideal” Strategy Models - using the Strategic Principles and Policies to drive out the “desired” Vision, Missions, Outcomes, Goals and Objectives •

• Strategy Visioning, Formulation and Development: - – Strategic Principles and Policies, Guidelines and Best Practices – Strategy Models and desired Vision, Missions, Outcomes, Goals and Objectives – Proposed Future Business Models and Value Propositions, Products and Services


10. PLANNING: = the bridge between the VISION and the ACTION – the “ACTION LINK” • Here, the Strategy team transforms the desired Vision, Missions, Outcomes, Goals and Objectives into the Strategic Master Plan, Enterprise Landscape Models, Strategic Roadmaps and Transition Plans for organisational readiness and mobilisation – maintaining Strategic Foresight mechanisms (Horizon Scanning, Monitoring and Tracking) to preserve the capability to quickly respond to fluctuations in internal and external environments •

• Strategy Enablement and Delivery Planning: - – Horizon Scanning, Monitoring and Tracking Systems and Infrastructure – Planned Future Business Models and Value Propositions, Products and

Services – Strategic Master Plan, Enterprise Landscape Models, Roadmaps and

Transition Plans

Thinking about the Future 11. ACTING • This penultimate phase is about communicating results and developing

action agendas for mobilising strategy delivery – thus launching Business Programmes that will drive forwards to the realisation of Strategic Master Plans and Future Business Models through Business Transformation, Enterprise Portfolio Management, Technology Refreshment and Service Management - via Cultural Change, innovative multi-tier and collaborative Business Operating Models, Emerging Technologies (Smart Devices, the Smart Grid and Cloud Services) Business Process Re-engineering and Process Outsource - Onshore / Offshore. •

• Strategy Enablement and Delivery Programmes: - – Launched Future Business Models and Value Propositions, Products and Services – Enterprise Portfolio Management - Technology Refreshment • System Management • – Business Transformation – Organisational Re-structuring • Cultural Change • Business

Process Management • Operating Models • Programme Planning & Contrl – DCT Models - Demand / Supply Models • Shared Services.• Business Process Outsource • – Emerging Technologies – Real-time Analytics • Smart Devices • Smart Grid • Mobile

Computing • Cloud Services • – Service Management - Service Access • Service Brokering • Service Provisioning •

Service Delivery •


12. REVIEWING • In this final phase, we focus on Key Lessons Learned and maintaining the flow of useful information from the Strategic Foresight mechanisms and infrastructure – in order to support an ongoing lean and agile capability to continually and successfully respond to the volatile and dynamic internal and external environment - through Futures Studies, Strategy Reviews, Business Planning and long-range Forecasting. •

We also prepare for the next round of the Strategy Cycle, beginning again with Phase 1 – Framing and Scoping.

• Strategy Review: - – Reviewed Business Models and Value Propositions, Products and Services – Horizon Scanning, Monitoring and Tracking Systems, Infrastructure and Data – Futures Studies, Strategy Reviews, Business Planning and long-range Forecasting

Peter Bishop and Andy Hines – University of Houston

Thinking about the Future TECHNICAL APPENDICES

Mechanical Processes –

Thermodynamics (Complexity and Chaos Theory) – governs the behaviour of Systems

Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles

Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures

Wave Mechanics (String Theory) – integrates the behaviour of every size and type of object

Research Philosophy



Thermodynamics (Complexity and Chaos Theory) – governs the behaviour of Systems randomness is as a result of Unknown Forces.....

Classical Mechanics (Newtonian Physics) – governs the behaviour of everyday objects – any apparent randomness is as a result of Unknown Forces.....

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles – all events are truly and intrinsically both symmetrical and random.....

Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures – any apparent randomness or asymmetry is as a result of Quantum Dynamics.....

Wave Mechanics (String Theory) – integrates the behaviour of every type of object –randomness and asymmetry is a result of Unknown Forces and Quantum Dynamics.....

Research Philosophies, Paradigms, Investigative and Analytic Methods

PROBABILISTIC versus DETERMINISTIC PARADIGMS

Rationalism – “blue-sky” pure research - the stance of the natural scientist Rationalism can be defined as “probabilistic research approaches that employ forensic and

analytical methods, make extensive use of both qualitative and quantitative analysis - free from

any pre-determined behavioral models - in order to discover hidden or unknown truths”

Positivism – goal seeking - the stance of the applied scientist Positivism can be defined as “deterministic research approaches that employ empirical methods,

and make extensive use of quantitative analysis, or develop logical calculi in order to develop

hypotheses and build conceptual models in support of formal explanatory theory”

Future Research Methods

• When undertaking any research of either a Scientific or Humanistic nature, it is most important for the researcher and supervisor to consider, compare and contrast all of the varied and diverse Research Philosophies and Paradigms, Data Analysis Methods and Techniques available - along with the express implications of their treatment of ontology and epistemology issues....,




Scepticism

Dogma

Enlightenment

Pragmatism

Realism

Social Sciences

Sociology

Economics





Life Sciences








Earth Sciences

Economic Analysis


Applied mathematics

Geography

Geology

Chemistry

Engineering


Archaeology

Palaeontology


Future Management




Astronomy

Cosmology

Relativity

Astrophysics

Astrology


Climate Change



Statistics

Strategic Foresight


Cluster Theory

Pure mathematics

Particle Physics

String Theory

Quantum Mechanics


Futures Studies


Reaction

Stoicism

Qualitative and Quantitative Methods


Qualitative Methods - tend to be deterministic, interpretive and subjective in nature. • When we wish to design a research project to investigate large volumes of unstructured data

producing and analysing graphical image and text data sets with a very large sample or set of information – “Big Data” – then the quantitative method is preferred. As soon as subjectivity - what people think or feel about the world - enters into the scope (e.g. discovering Market Sentiment via Social Media postings), then the adoption of a qualitative research method is vital. If your aim is to understand and interpret people’s subjective experience and the broad range of meanings that attach to it, then interviewing, observation and surveying a range of non-numerical data (which may be textual, visual, aural) are key strategies you will consider. Research approaches such as using focus groups, producing case studies, undertaking narrative or content analysis, participant observation and ethnographic research are all important qualitative methods. You will also want to understand the relationship of qualitative data to numerical research. Any qualitative methods pose their own problems with ensuring the research produces valid and reliable results (see also: Analytics - Working with “Big Data”).

Quantitative Methods - tend to be probabilistic, analytic and objective in nature. • When we want to design a research project to tests a hypothesis objectively by capturing and

analysing numerical data sets with a large sample or set of information – then the quantitative method is preferred. There are many key issues to consider when you are designing an experiment or other research project using quantitative methods, such as randomisation and sampling. Also, quantitative research uses mathematical and statistical means extensively to produce reliable analysis of its results (see also: Cluster Analysis and Wave-form methods).



Futures Studies

Numeric Definitive

Quantitative


Investigative

Descriptive

Analytic

Social Sciences

Sociology

Economics





Life Sciences




Biology Ecology

Climate Change



Earth Sciences


Applied mathematics

Future Management




Weather Forecasting

Particle Physics

String Theory

Statistics

Strategic Foresight







Astronomy

Cosmology

Relativity

Astrophysics


Geography

Geology

Archaeology

Economic Analysis


Chemistry

Engineering

Astrology

Geo-physics


Palaeontology

Cluster Theory

Interpretive

Qualitative


Quantum Mechanics


Ontology

• Ontology is concerned with nature of reality. Ontology raises questions about the

assumptions that researchers have about the way the world operates - and their

commitment to particular viewpoints. The two aspects of ontology we describe here will

both have their devotees among business and management researchers. In addition,

both are likely to be accepted as producing valid knowledge by many researchers

– The first aspect of ontology that we need to discuss is objectivism - which supports a

position that social entities and phenomena exist in reality - independent from, and

external to, the various social actors who are concerned with their existence.

– The second aspect, subjectivism , holds that social phenomena are created from the

perceptions and consequent actions of those social actors concerned with their existence

• Blaikie (1993) describes the root definition of ontology as ‘the science or study of being’

and develops this description for the social sciences to encompass ‘claims and

assumptions, both valid and invalid, about what does and does not exist, what it may or

may not look like, what units make it up and how these units interact with each other’. In

short, ontology encompasses both reality and the intimate and very personal viewpoint of

reality held by ourselves and our fellow researchers (valid or invalid, learned, claimed or

assumed) - along with our opinions as to the true nature of reality.

Ontology

• Hatch and Cunliffe (2006) use both an everyday example, and a social science example

to illustrate this point. For the everyday example, they use the example of a workplace

report – asking the subject to question whether it describes what is really going on in the

workplace - or only what the author thinks may be going on. They go on to highlight the

complexity that is introduced when considering phenomena such as personality types,

social class, culture, education, power and control, and what really exists and what is

simply an illusion. Further extending the discussion as to how individuals (and groups)

determine these realities – does this reality exist only through experience of it

(subjectivism), or does it exist independently of those who live with it (objectivism).

• Specifically, we need to ask if this is an objective reality that really exists, or is it only a

subjective reality created in our own minds. We all have a number of deeply embedded

ontological assumptions which will affect our view on what is real and whether we

attribute existence to one set of things over another set of things. If these underlying

assumptions are not identified, exposed and considered, then the researcher may be

blinded to certain aspects of the inquiry or certain phenomena - since those aspects or

phenomena may be somewhat taken for granted - implicit, assumed, thus not opened up

for question, consideration - or even discussion.

Ontology is concerned with nature of reality - and raises questions about assumptions

Epistemology

• Closely coupled with ontology and its consideration of what constitutes reality - epistemology

considers views about the most appropriate ways of enquiring into the nature of the world

(Easterby-Smith, Thorpe and Jackson, 2008) and ‘what is knowledge and what are the sources

and limits of knowledge’ (Eriksson and Kovalainen, 2008). Questions of epistemology begin to

consider the research method, and Eriksson and Kovalainen go on to discuss how

epistemology defines how knowledge can be produced and argued for.

• Blaikie (1993) describes epistemology as ‘the theory or science of the method or grounds of

knowledge’ - expanding this into a set of claims or assumptions about the ways in which it is

possible to gain knowledge of reality, how what exists may be known, what can be known, and

what criteria must be satisfied in order to be described as knowledge.

• Chia (2002) describes epistemology as ‘how and what it is possible to know’ and the need to

reflect on methods and standards through which reliable and verifiable knowledge is produced.

• Hatch and Cunliffe (2006) summarise epistemology as ‘knowing how you can know’ and

expand this by asking how is knowledge generated, what criteria discriminate good knowledge

from bad knowledge, and how should reality be represented or described. They go on to

highlight the inter-dependent relationship between epistemology and ontology, and how one

both informs, and depends upon, the other.

Epistemology concerns what constitutes acceptable knowledge in a field of study.

Research Paradigms


Primary

Futures

Disciplines

9.

Future of Philosophy,

Knowledge & Values

7 .

Future of Information &

Knowledge Management

10. Future Beliefs –

Moral, Ethical

& Religious Futures

1. Futures Studies

4.

Science and

Technology Futures

12. Human Futures –

Sociology, Anthropology

and Cultural Studies

3.

Political & Economic

Futures

6.

Entrepreneurship &

Innovation Futures

2. Strategic Foresight

5.

Environment, Climate &

Ecology Futures

8.

Personal Futures –

Trans-humanism

11. Massive Change –

Human Impact and

Global Transformation


• Futures Studies – History and Analysis of Prediction – Future Studies – Classification and Taxonomy – Future Management Primary Disciplines – Future Management Secondary Specialisations

• Strategic Foresight – Foresight Regimes, Frameworks and Paradigms – Foresight Models, Methods, Tools and Techniques

• Quantitative Techniques • Qualitative Techniques • Chaos Theory – Random Events, Uncertainty and Disruption

• Political and Economic Futures • Science and Technology Futures • Entrepreneurship and Innovation Futures • Personal Futures – Trans-humanism, NLP / EHT • The Future of Philosophy, Knowledge and Values • Future Beliefs – Moral, Ethical and Religious Futures • Massive Change – Human Impact and Global Transformation • Human Futures – Sociology, Anthropology and Cultural Studies • The Future of Information, Knowledge Management and Decision Support


• Monte Carlo Simulation • Forecasting and Foresight • Back-casting and Back-sight • Causal Layered Analysis (CLA) • Complex Adaptive Systems (CAS) • Political Science and Policy Studies • Linear Systems and Game Theory • War-gaming and Lanchester Theory • Complex Systems and Chaos Theory • Integral Studies and Future Thinking • Critical and Evidence-Based Thinking • Predictive Surveys and Delphi Oracle • Visioning, Spontaneity and Creativity • Foresight, Intuition and Pre-cognition • Developmental & Accelerative Studies • Systems & Technology Trends Analysis • Scenario Planning and Impact Analysis • Collaboration, Facilitation & Mentoring

• Black Swan Events - Weak Signals, Wild Cards, Chaos, Uncertainty & Disruption

• Economic Modelling & Planning • Financial Planning and Analysis • Ethics of Emerging Technology Studies • Horizon Scanning, Tracking & Monitoring • Intellectual Property and Knowledge • Critical Futures and Creative Thinking • Emerging Issues and Technology Trends • Patterns, Trends & Extrapolation Analysis • Linear Systems & Random Interactions • Cross Impact Analysis and Factors of

Global Transformation and Change • Preferential Surveys / Polls and Market

Research, Analysis and Prediction • The Future of Religious Beliefs - Theology,

Divinity, Ritual, Ethics and Value Studies • Divination – Hermetic, Mystic, Esoteric

and Enlightened Spiritual Practices


• Science and Technology Futures • The Cosmology Revolution

– Dark Energy, Dark Mass – String Theory and the Nature of Matter

• SETI – The Search for Extra-Terrestrial Planetary Systems, Life and Intelligence

• Nano-Technology, Nuclear Physics and Quantum Mechanics

• The Energy Revolution - Nuclear Fusion Hydrolysis and Clean Energy

• Science and Society Futures – the Social Impact of Technology

• Smart Cities of the Future • The Information Revolution – Internet

Connectivity and the Future of the Always-on Digitally Connected Society

• Digital Connectivity, Smart Devices, the Smart Grid & Cloud Computing Futures

• Content Analysis (“Big Data”) – Data Set “mashing”, Data Mining & Analytics

• Earth and Life Sciences – the Future of Biology, Geology & Geographic Science

• Environmental Sustainability Studies – Climatology, Ecology and Geography

• Human Activity – Climate Change and Future Environmental Degradation – Desertification and De-forestation

• Human Populations - Profiling, Analysis, Streaming and Segmentation

• Human Futures - Population Drift and Urbanisation - Human Population Curves and Growth Limit Analysis

• The Future of Agriculture, Forestry, Fisheries, Agronomy & Food Production

• Terrain Mapping and Land Use – Future of Topology, Topography & Cartography

• Future Natural Landscape Planning, Environmental Modelling and Mapping

• Future Geographic Information Systems, Spatial Analysis & Sub-surface Modelling


• Macro-Economic and Financial Futures • Micro-Economic and Business Futures • Strategic Visioning – Possible, Probable &

Alternative Futures • Strategy Design – Vision, Mission and

Strategy Themes • Strategy Development – Outcomes, Goals

and Objectives • Performance Management – Target Setting

and Action Planning • Critical Success Factors (CSF’s) and Key

Performance indicators (KPI’s) • Business Process Management (BPM) • Balanced Scorecard Method • Planning and Strategy

– (foundation, intermediate & advanced)

• Modelling and Forecasting – (foundation, intermediate & advanced)

• Threat Assessment & Risk Management – (foundation, intermediate & advanced)

• Layers of Power, Trust and Reputation • Leadership Studies, Goal-seeking and

Stakeholder Analysis • Military Science, Peace and Conflict

Studies – War, Terrorism and Insecurity • Corporate Finance and Strategic

Investment Planning Futures • Management Science and Business

Administration Futures • Future Management and Analysis of Global

Exploitation of Natural Resources • Social Networks and Connectivity • Consumerism and the rise of the new

Middle Classes • The BRICs and emerging powers

– • Brazil • Russia • India • China • • The Seven Waves of Globalisation

– • Goods • People • Capital • Services – • Ideology • Economic Control •

– • Geo-Political Domination •


• Human Values, Ethics and Beliefs • History, Culture and Human Identity • Human Geography & Industrial Futures • Human Factors and Behavioural Theory • Anthropology, Sociology and Factors of

Cultural Change • Human Rites, Rituals and Customs - the

Future of Cults, Sects and Tribalism • Ethnographic and Demographic Futures • Epidemiology, Morbidity and Actuarial

Science Futures • Infrastructure Strategy, Regional Master

Planning and Urban Renewal • Future Townscape Envisioning. Planning

Modelling and Virtual Terrain Mapping • The Future of Urban and Infrastructure

Master Planning, Zoning and Control • Architecture and Design Futures - living

in the Built Environment of the Future

• Trans-humanism – The Future Human State – Qualities, Capabilities, Capacities

• The Future of Medical Science, Bio-Technology and Genetic Engineering

• The Future of the Human Condition - Health, Wealth and Wellbeing

• The Future of Biomechanics, Elite Sports and Professional Athletics

• Personal Futures – Motivational Studies, Life Coaching and Personal Training

• Positive Thinking – Self-Awareness, Self-Improvement & Personal Development

• Positive Behavioural Psychology and Cognitive Therapy - NLP and EHT

• Intuitive Assimilation and Cognitive Analysis

• Predictive Envisioning and Foresight Development

• Contemplative Mediation and Psychic Methods


• Business Strategy, Transformation and Programme Management Futures

• Next Generation Enterprises (NGE) – Envisioning, Planning and Modelling

• Multi-tier Collaborative Future Business Target Operating Models (eTOM)

• Corporate Responsibility / Triple Bottom Line Management

• Regulatory Compliance - Enterprise Governance, Reporting and Controls

• Future Economic Modelling, Long-range Forecasting and Financial Analysis

• The Future of Organisational Theory and Operational Analysis

• Business Innovation and Product Planning Futures

• Technology Innovation and Product Design Futures

• Product Engineering and Production Planning Futures

• Enterprise Resource Planning and Production Management Futures

• Marketing Needs Analysis, Propositions and Product Life-cycle Management

• The Future of Marketing Services, Communications and Advertising

• The Future of Media, Entertainment and Multi-channel Communications

• The Future of Leisure, Travel & Tourism – Culture, Restaurants and Entertainment

• The Future of Spectator Events - Elite Team Sports and Professional Athletics

• The Future of Art, Literature and Music • The Future of Performance Arts, Theatre

and the Moving Image • Science Fiction & Images of the Future • Interpreting Folklore, Legends & Myths –

Theology, Numerology & Lexicography • Utopian and Dystopian Literature, Film

and Arts




Scepticism

Dogma

Enlightenment

Pragmatism

Realism

Social Sciences

Sociology

Economics





Life Sciences








Earth Sciences

Economic Analysis


Applied mathematics

Geography

Geology

Chemistry

Engineering


Archaeology

Palaeontology


Future Management




Astronomy

Cosmology

Relativity

Astrophysics

Astrology


Climate Change



Statistics

Strategic Foresight


Cluster Theory

Pure mathematics

Particle Physics

String Theory

Quantum Mechanics


Futures Studies


Reaction

Stoicism

DETERMINISTIC Versus PROBABILISTIC FUTURES

STRATEGIC MANAGEMENT tends to be Deterministic in nature - 12 Deterministic Futures.....

DETERMINISTIC FUTURE VIEWPOINTS


• Humanist (Instructional) Paradigm - Sceptic Futurism

• Dogmatic (Theosophical) Paradigm - Reactionary Futurism

• Utilitarian (Consequential) Paradigm – Egalitarian Futurism


• Steady State (La meme chose - same as it ever was) Paradigm – Constant Futurism

• Hellenistic (Classical) Paradigm – Future of Human Ethics, Morals, Values and Beliefs




• Empirical (Scientific Determinism, Theoretical Positivism) Paradigm – Hypothetical Futurism

• Predictive (Ordered, Systemic, Mechanistic, Enthalpy) Paradigm – Deconstructionist Futurism

DETERMINISTIC Versus PROBABILISTIC FUTURES

FUTURES STUDIES tends to be Probabilistic in nature - 12 Probabilistic Futures.....

PROBABILISTIC FUTURE VIEWPOINTS

• Polemic (Rational) Paradigm - Enlightened Futurism


• Postmodernism (Reactionary) Paradigm - Structural Futurism



• Mystic (Gnostic, Sophistic, Esoteric, Cathartic) Paradigm – Contemplative Futurism

• Uncertainty (Random, Chaotic, Disorderly, Enthalpy) Paradigm - Disruptive Futurism

• Experiential (Forensic, Deductive, Realist, “Blue Sky”) Paradigm – Pragmatic Futurism



• Interpretive (Ordered, Systemic, Mechanistic, Entropic) Paradigm – Constructive Futurism




Futures Studies

Numeric Definitive

Quantitative


Investigative

Descriptive

Analytic

Social Sciences

Sociology

Economics





Life Sciences




Biology Ecology

Climate Change



Earth Sciences


Applied mathematics

Future Management




Weather Forecasting

Particle Physics

String Theory

Statistics

Strategic Foresight







Astronomy

Cosmology

Relativity

Astrophysics


Geography

Geology

Archaeology

Economic Analysis


Chemistry

Engineering

Astrology

Geo-physics


Palaeontology

Cluster Theory

Interpretive

Qualitative


Quantum Mechanics


Future Management Methods and Techniques

Throughout eternity, all that is of like form comes around

again – everything that is the same must return again in

its own everlasting cycle.....

• Marcus Aurelius – Emperor of Rome •

Introduction

Futures Studies Research

Changement est vieux comme le monde….. le changement est aussi vieux que le temps.

//upload.wikimedia.org/wikipedia/commons/2/22/An_Experiment_on_a_Bird_in_an_Air_Pump_by_Joseph_Wright_of_Derby,_1768.jpg


“Research philosophy is an over-arching term relating to the

development of knowledge - and understanding the nature of that

knowledge which is under development.....”

• Adapted from Saunders et al, (2009) •

Epistemology concerns the scope of what constitutes acceptable knowledge in a field of study.

Ontology is concerned with the nature of reality - and raises questions about assumptions


• This section aims to discuss Risk Research Philosophies in detail, in order to develop

a general awareness and understanding of the options - and to describe a rigorous

approach to Research Methods and Scope as a mandatory precursor to the full Risk

Research Design. Kvale (1996) and Denzin and Lincoln (2003) highlight how different

Research Philosophies can result in much tension amongst research stakeholders.













• James and Vinnicombe (2002) caution that we all have our own inherent preferences

that are likely to shape our research designs and conclusions, Blaikie (2000) describes

these aspects as part of a series of choices that the researcher has to consider, and

demonstrates that this alignment that must connect choices made back to the original

Research Problem. If this is not achieved, then certain research methods may be

adopted which turn out to be incompatible with the researcher’s stance, and result in

the final work being undermined through lack of coherence and consistency.

• Blaikie (1993) argues that Research Methods aligned to the original Research Problem

are highly relevant to Social Science since the humanistic element introduces a

component of “free will”’ that adds a complexity beyond those usually encountered in

the natural sciences – whilst others, such as Hatch and Cunliffe (2006) draw attention

to the fact that different paradigms ‘encourage researchers to study phenomena in

different ways’, going on to describe a number of organisational phenomena from three

different perspectives, thus highlighting how different kinds of knowledge may be

derived through observing the same phenomena from different philosophical

viewpoints and perspectives.

Aspects of Research Philosophy

• Rationalism – “blue-sky” pure research - the stance of the natural scientist – Rationalism can be defined as “probabilistic research approaches that employ forensic and

analytical methods, make extensive use of both qualitative and quantitative analysis - free from any pre-determined behavioral models - in order to discover hidden or unknown truths”

• Positivism – goal seeking - the stance of the applied scientist – Positivism can be defined as “deterministic research approaches that employ empirical

methods, and make extensive use of quantitative analysis, or develop logical calculi in order to develop hypotheses and build conceptual models in support of formal explanatory theory”

• Realism – direct and critical realism – The essence of realism is that what the senses show us as reality is the truth; that objects

have an existence independent of the human mind.

• Interpretation – researchers as ‘social actors’ – Interpretation advocates the necessity for researchers to understand differences between


• Pragmatism – studies judgements about value – Pragmatism holds that the most important determinant of the epistemology, ontology,

axiology adopted is the research question

Research Paradigms Metaphysical Philosophy


metaphysical

philosophy

Related: -

Alchemy

Ontology

Taxonomy

Classification

Natural History

Natural Philosophy

Systemic

Methodology

Rationalism can be defined as “probabilistic research

approaches that employ forensic and analytical

methods, make extensive use of both qualitative and

quantitative analysis - free from any pre-determined

behavioural models - in order to discover the secrets

of hidden or “unknown” truths

Positivism can be defined as “deterministic research

approaches that employ empirical methods, and

make extensive use of quantitative analysis, or

develop logical calculi in order to develop hypotheses

and build conceptual models in support of formal

explanatory theory”

The essence of Realism is that what the senses

show us as reality is the truth; that objects have an

existence independent of the human mind.

Interpretation advocates the necessity for

researchers to understand differences between


Pragmatism holds that the most important

determinant of the epistemology, ontology, axiology

adopted is the question posed by the research

Rationalism – “blue-sky” research -

the natural stance of the free and

unencumbered “pure” scientist

Positivism – goal seeking - the

natural stance of the restricted and

constrained “applied” scientist

Realism – the direct, critical and

objective science of realism

Interpretation – scientific

researchers as “social actors”

Pragmatism – studies subjective

judgements about questions of

ethics, values and beliefs

Saunders et al, (2009)

Futures Research Philosophies and Investigative Methods

Qualitative and Quantitative Investigative Methods

Qualitative Methods: –

tend to be deterministic, interpretive and subjective in nature.

Quantitative Methods: –

tend to be probabilistic, analytic and objective in nature.....

Futures Research Methods

• When undertaking any research of either a Scientific or Humanistic nature, it is most important for the researcher and supervisor to consider, compare and contrast all of the varied and diverse Research Philosophies and Paradigms, Data Analysis Methods and Techniques available - along with the express implications of their treatment of ontology and epistemology issues....,


Research Study Roles and Responsibilities

• Supervisor – authorises and directs the Futures Research Study.

• Project Manager – plans and leads the Futures Research Study.

• Moderator – reviews and mentors the Futures Research Study.

• Researcher – undertakes the detailed Futures Research Tasks.

• Research Aggregator – examines hundreds of related Research

papers - looking for hidden or missed Findings and Extrapolations.

• Author – compiles, documents and edits the Research Findings.

Futures Research Philosophies and Investigative Methods


Thermodynamics (Complexity and Chaos Theory) – governs the behaviour of Systems randomness is as a result of Unknown Forces.....

Classical Mechanics (Newtonian Physics) – governs the behaviour of everyday objects – any apparent randomness is as a result of Unknown Forces.....

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles – all events are truly and intrinsically both symmetrical and random.....

Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures – any apparent randomness or asymmetry is as a result of Quantum Dynamics.....

Wave Mechanics (String Theory) – integrates the behaviour of every type of object –randomness and asymmetry is a result of Unknown Forces and Quantum Dynamics.....

Philosophical Paradigms Philosophy Description Leading figures Quantitative

Paradigm

In the Quantitative Paradigm, the design of a

research study begins with the selection of a research

topic (subject) and a research paradigm (method).

Quantitative Research refers to the systematic

empirical investigation of social and scientific

phenomena through system modelling and statistical

analysis - via direct observation and careful collection

of mathematical, numerical or biometric datasets, and

thorough analysis and interpretation of the data.

Scientific Research observes and

collects data on the behaviour of a

system, formulates a hypothesis to

explain the observed behaviour, and

then designs and executes an

experiment to test how well his

hypothesis predicts the actual and

real observations and outcomes.

Qualitative

Paradigm

Qualitative Paradigm. Most qualitative research

texts identify three primary types of research:-

1. Exploratory – research on a concept, people, or

situation that the researcher knows little about.

2. Descriptive (Narrative) – research on a concept,

people, or situation that the researcher knows

something about, but just wants to describe the

findings that he/she has found or observed.

3. Explanatory – involves deriving a hypothesis

from existing theories and available models, then

testing that hypothesis through a process of

experimental observation and data collection.



Qualitative Methods - tend to be deterministic, interpretive and subjective in nature. • When we wish to design a research project to investigate large volumes of unstructured data

producing and analysing graphical image and text data sets with a very large sample or set of information – “Big Data” – then the quantitative method is preferred. As soon as subjectivity - what people think or feel about the world - enters into the scope (e.g. discovering Market Sentiment via Social Media postings), then the adoption of a qualitative research method is vital. If your aim is to understand and interpret people’s subjective experience and the broad range of meanings that attach to it, then interviewing, observation and surveying a range of non-numerical data (which may be textual, visual, aural) are key strategies you will consider. Research approaches such as using focus groups, producing case studies, undertaking narrative or content analysis, participant observation and ethnographic research are all important qualitative methods. You will also want to understand the relationship of qualitative data to numerical research. Any qualitative methods pose their own problems with ensuring the research produces valid and reliable results (see also: Analytics - Working with “Big Data”).

Quantitative Methods - tend to be probabilistic, analytic and objective in nature. • When we want to design a research project to tests a hypothesis objectively by capturing and

analysing numerical data sets with a large sample or set of information – then the quantitative method is preferred. There are many key issues to consider when you are designing an experiment or other research project using quantitative methods, such as randomisation and sampling. Also, quantitative research uses mathematical and statistical means extensively to produce reliable analysis of its results (see also: Cluster Analysis and Wave-form methods).



Futures Studies

Numeric Definitive

Quantitative


Investigative

Descriptive

Analytic

Social Sciences

Sociology

Economics





Life Sciences




Biology Ecology

Climate Change



Earth Sciences


Applied mathematics

Future Management




Weather Forecasting

Particle Physics

String Theory

Statistics

Strategic Foresight







Astronomy

Cosmology

Relativity

Astrophysics


Geography

Geology

Archaeology

Economic Analysis


Chemistry

Engineering

Astrology

Geo-physics


Palaeontology

Cluster Theory

Interpretive

Qualitative


Quantum Mechanics



• Blue skies research (also called blue sky science) is pure scientific research

in those domains where "real-world" applications are not always immediately

apparent. This has been defined as "research without a clear goal" and

"curiosity-driven science." Blue skies research may sometimes be used

interchangeably with the term "basic research“ or “fundamental research”.

• Proponents of this probabilistic mode of science argue that unanticipated

scientific breakthroughs are sometimes more valuable than the outcomes of

agenda-driven (empirical, deterministic) research – thus heralding advances

in genetics and stem cell biology as examples of the unforeseen benefits of

Human Genome Mapping research - which was originally intended as being

purely theoretical in scope.

• The inherently uncertain return on investment of blue-sky projects render them

both politically and commercially unpopular - for example SETI, the Search for

Extra-Terrestrial Intelligence – thus they tend to lose funding to more reliably

practical, pragmatic and profitable research. The specific name "blue skies" to

describe this kind of research is mainly used in Great Britain.

http://en.wikipedia.org/wiki/Basic_research

http://en.wikipedia.org/wiki/Science

http://en.wikipedia.org/wiki/Genetics

http://en.wikipedia.org/wiki/Stem_cell

http://en.wikipedia.org/wiki/Return_on_investment

Futures Research Philosophies

and Investigative Methods • This section aims to discuss Futures Research Philosophies in detail, in order to

develop a general awareness and understanding of the options - and to describe a

rigorous approach to Research Methods and Scope as a mandatory precursor to the

full Research Design. Denzin and Lincoln (2003) and Kvale (1996) highlight how

different Research Philosophies can result in much tension amongst stakeholders.












Futures Studies

• Futures Studies, Foresight, or Futurology is the practice and art of postulating possible, probable, and preferable future outcomes. Futures studies (colloquially referred to as "Futures" by many of the field's practitioners) seeks to understand what is likely to continue, what is likely to change, and what will be completely new and novel. Part of the discipline seeks to develop a systematic and pattern-based understanding of the past, present and future, and thus attempts to determine both the content (description) and probability (likelihood of occurrence) of a wide range of possible, probable and alternative future outcomes, scenarios, events and trends.

• Futures is an interdisciplinary curriculum, studying yesterday's and today's changes, through aggregating and analysing both lay and professional strategies, views and opinions about the future with respect to what may happen tomorrow. This includes analysing the sources, patterns, and causes of change and stability in an attempt to develop foresight and to map possible futures. Futures Studies has been greatly enhanced by the recent arrival of “Big Data” technologies – which automates the process of Horizon (human domains) and Environment (natural domains) futures research - scanning, monitoring and tracking massive volumes of global Internet Content, Social Media Postings, RSS News Feeds and other Data Streams in order to discover “Weak Signals” and “Wild Cards” – predicators of future change.

• Around the world the field is variously referred to as futures studies, strategic foresight, futurology, futuristics, futures thinking, futuring, futuribles (in France, the latter is also the name of the important 20th century foresight journal published only in French), and prospectiva (in Spain and Latin America). Futures studies (and one of its sub-disciplines, strategic foresight) are the academic field's most commonly used terms.

Forecasting and Prediction • Forecasting is the process of logical estimation of events in unknown

future situations. Prediction is a similar, but less rigorous term. Both may refer to estimation of time series, cross-sectional or longitudinal datasets.

• Usage can differ between areas of application: for example in hydrology, the terms "forecast" and "forecasting" are sometimes reserved for the logical projection of probable values at certain specific future times, while the term "prediction" may be used for more general estimates - such as the number of times flooding will occur over a given (longer) period.

• Risk and uncertainty are central to forecasting and prediction. Scenarios and Mathematical models are both used in the practice of Forecasting for every day events such as weather forecasting for agriculture and shipping, and business performance forecasting for industry and commerce. The discipline of demand planning, also sometimes referred to as supply chain forecasting, embraces both statistical forecasting and scenario analysis.

• Forecasting is commonly used in discussion of time-series data where the timeline extends over historic (past), current (present) and future events.

http://en.wikipedia.org/wiki/Estimation

http://en.wikipedia.org/wiki/Prediction

http://en.wikipedia.org/wiki/Time_series

http://en.wikipedia.org/wiki/Cross-sectional_data



http://en.wikipedia.org/wiki/Longitudinal_study

http://en.wikipedia.org/wiki/Hydrology

http://en.wikipedia.org/wiki/Future

http://en.wikipedia.org/wiki/Risk

http://en.wikipedia.org/wiki/Uncertainty

Goal-seeking and Back-casting

• Back-casting starts with defining a desirable future and then works backwards to identify policies and programs that will connect that desired future to the present situation. The empirical question of back-casting asks: - "if we want to attain a certain goal or set of objectives, what actions must be taken in order to facilitate our journey and arrive there?“

• Forecasting is the process of predicting the future based on extrapolating current patterns and trends. Back-casting approaches the challenge of describing the future from the opposite direction: - “a method in which the future desired conditions are envisioned and steps are then defined to attain those conditions - rather than taking steps that are merely a continuation of present methods extrapolated into the future”

• Goal-seeking and Back-casting are a key component of the Soft Path, a concept developed by Amory Lovins in response to the shock of the 1973 energy crisis in the United States. Goal-seeking and Back-casting has been further developed, refined and deployed by various Future Research Groups e.g. “The Natural Step” (TNS) Framework.

• Back-casting is increasingly used in urban planning and resource management of water and energy. In 2006, the Capital Regional District Water Services, which services the greater Victoria area in British Columbia, Canada, committed to back-casting from the year 2050 as a formal element of all future strategic water planning initiatives.

• The POLIS Water Sustainability Project has developed a soft path planning calculator that uses a back-casting framework http://www.poliswaterproject.org/toolkit which was released in the Autumn of 2009.

http://en.wikipedia.org/wiki/Amory_Lovins

http://en.wikipedia.org/wiki/Amory_Lovins

http://en.wikipedia.org/wiki/1973_energy_crisis

http://en.wikipedia.org/wiki/Capital_Regional_District

http://en.wikipedia.org/wiki/Victoria

http://en.wikipedia.org/wiki/British_Columbia

http://en.wikipedia.org/wiki/Canada

http://en.wikipedia.org/wiki/POLIS_Water_Sustainability_Project

http://www.poliswaterproject.org/toolkit

Goal-seeking and Back-casting

• Research Groups that deploy Back-casting Frameworks: –

– Global Scenario Group

– Institute for Sustainable Futures http://www.isf.uts.edu.au/

– Pacific Institute

– POLIS Project on Ecological Governance

– POLIS Water Sustainability Project - The POLIS Water Sustainability Project

developed a soft path planning calculator that uses a back-casting framework -

http://www.poliswaterproject.org/toolkit - which was released in Autumn 2009.

– Tellus Institute

• environmental research group that uses back-casting techniques to

develop sustainability strategies

– The Natural Step” (TNS) Framework http://www.forumforthefuture.org

• sustainability research group that uses a back-casting framework to

develop strategies for the environment

– Thinking portal

http://en.wikipedia.org/wiki/Global_Scenario_Group

http://en.wikipedia.org/wiki/Global_Scenario_Group

http://www.isf.uts.edu.au/

http://en.wikipedia.org/wiki/Pacific_Institute

http://en.wikipedia.org/wiki/POLIS_Project_on_Ecological_Governance



http://www.poliswaterproject.org/toolkit

http://www.tellus.org/



http://www.forumforthefuture.org/

http://en.wikipedia.org/wiki/Portal:Thinking

Foresight • In Futures Studies, the term "Foresight " embraces: -

– Influencing public policy and strategic direction (“Shaping the future”)

– Critical thinking concerning long-term policy development (planning)

– Debate and consultation to create wider stakeholder participation (networking)

• Foresight is being applied to strategic activities in both the public and the private sector, and stresses the need to link every activity or project with any kind of future dimension towards taking action today (the action link) in order to make a planned, integrated future impact (“shaping the future”) possible.

• Foresight differs from much futures research and strategic planning, as it combines a range of approaches that encompasses the three key components highlighted above, which may be recast as: -

– futures (forecasting, forward thinking, perspectives) tools and methods

– planning (strategic analysis, priority setting) timelines and roadmaps

– networking (participatory, dialogic) inclusion and orientation

• Much futures research has been academic, but many Foresight programmes were designed to research Risk and influence Public Policy or explore Disruptive Change and influence Research and Development policy in industry. In the past some technology policy research has been very highly focused. Foresight attempts to go beyond the normal boundaries and gather much more widely distributed intelligence.

Foresight • Foresight draws on traditions of work in long-range forecasting and strategic

planning horizontal policymaking and democratic planning, horizon scanning and futures studies (Aguillar-Milan, Ansoff, Feather, van der Hijden, Slaughter et all) - but was also highly influenced by systemic approaches to innovation studies, global design, massive change, science and technology futures, economic, social and demographic policy, fashion and design - and the analysis of "weak signals" and "wild cards", "future trends“ "critical technologies“ and “cultural evolution".

– The longer-term: - futures that are usually at least 10 years away (though there are some exceptions to this, especially in its use in private business). Since Foresight is an action-oriented discipline (via the planning link) it will rarely be applied to perspectives beyond a few decades out. Where major infrastructure decisions such as petrology reservoir exploitation, aircraft design, power station construction, transport hubs and town master planning decisions are concerned - then the planning horizon may well be half a century.

– Alternative futures: - it is helpful to examine alternative paths of development, not just what is currently believed to be most likely or business as usual. Often Foresight will construct multiple scenarios. These may be an interim step on the way to creating what may be known as positive visions, success scenarios or aspirational futures. Sometimes alternative scenarios will be a major part of the output of a Foresight study, with the decision about what preferred future to build being left to other mechanisms (Planning and Strategy).

Foresight and Back-sight

• Foresight is the process of understanding the future based on extrapolation of current patterns and trends along with the analysis of the casual agents of random events and the contributory factors towards disruptive change.

• Back-sight approaches the challenge of examining the current state from the opposite direction - a method in which the current adverse conditions and its causes are analysed and then steps are identified that may have prevented those adverse conditions arising, mitigated the impact of those adverse conditions or simply avoided the consequences of those adverse conditions.

• Back-sight. In a post-apocalyptic Black Swan Event Scenario, we can use Causal Layer Analysis (CLA) techniques to review our Risk Analysis and Management Strategies in order to identify those Weak Signals which may have indicated subsequent Wild Cards – risk events which have a very low probability of occurring, but an inordinately high impact when they do happen – in order to determine future improvements and enhancements to Enterprise Risk Management Frameworks.

• Back-sight examines a Black Swan Event or Wild Card Scenario and then works backwards to identify those actions, policies, agents for change and events that connected the past to the present. The fundamental question of back-casting asks: "if we want to mitigate undesirable outcomes, what future actions could have be taken to avoid it happening or to reduce its impact?“

Strategic Foresight • Strategic Foresight is the ability to create and maintain a high-quality, coherent

and functional forward view, and to use the insights arising in useful organisational ways. For example to detect adverse conditions, guide policy, shape strategy, and to explore new markets, products and services. It represents a fusion of futures methods with those of strategic management (Slaughter (1999), p.287).

– Probabilistic Futures (Rational Futurism) – assessing possible, probable and alternative futures – selecting those futures offering conditions that best fit our strategic goals and objectives for achieving a preferred and desired future. Filtering for a more detailed analysis may be achieved by discounting isolated outliers and focusing upon those closely clustered future descriptions which best support our desired future outcomes, goals and objectives.

• Strategy Visioning – Future outcomes, goals and objectives are defined via Strategic Foresight and are determined by design, planning and management - so that the future becomes realistic and achievable. Possible futures may comply with our preferred options - and therefore our vision of an ideal future and desired outcomes could thus be fulfilled.

– Deterministic Futurism (Strategic Positivism) – articulating a single, preferred vision of the future. The future will conform to our preferred options - thus our vision of an ideal future and desired outcomes will be fulfilled.

Weak Signals and Wild Cards

• “Wild Card” and "Black Swan" manifestations are extreme and unexpected events which have a very low probability of occurrence, but an inordinately high impact when they do happen. Trend-making and Trend-breaking agents or catalysts of change may predicate, influence or cause wild card events which are very hard - or even impossible - to anticipate, forecast or predict.

• In any chaotic, fast-evolving and highly complex global environment, as is currently developing and unfolding across the world today, the possibility of any such "Wild Card” or "Black Swan" events arising may, nevertheless, be suspected - or even expected. "Weak Signals" are subliminal indicators or signs which may be detected amongst the background noise - that in turn point us towards any "Wild Card” or "Black Swan" random, chaotic, disruptive and / or catastrophic events which may be on the horizon, or just beyond......

• Back-casting and Back-sight: - In any post-apocalyptic Black Swan Event Scenario, we can use Causal Layer Analysis (CLA) techniques to analyse and review our Risk Management Strategies – in order to identify those Weak Signals which may have predicted, suggested, pointed towards or indicated subsequent Wild Cards or Black Swan Events – and so discover changes and improvements to strengthen and enhance our Enterprise Risk Management Frameworks.

Horizon Scanning, Tracking and Monitoring

• Are you all at sea over your future.....?

Horizon Scanning • Horizon Scanning is an important technique for establishing a sound knowledge

base for planning and decision-making. Anticipating and preparing for the future – uncertainty, threats, challenges, opportunities, patterns, trends and extrapolations – is an essential core component of any organisation's long-term sustainability strategy.

• What is Horizon Scanning ?

Horizon Scanning is defined by the UK Government Office for Science as: -

“the systematic examination of potential threats, opportunities and likely future developments, including (but not restricted to) those at the margins

of current thinking and planning”.

• Horizon Scanning may explore novel and unexpected issues as well as persistent problems or trends. The government's Chief Scientific Adviser is encouraging Departments to undertake horizon scanning in a structured and auditable manner.

• Horizon Scanning enables organisations to anticipate and prepare for new risks and opportunities by looking at trends and information in the medium- to long-term future.

• The government's Horizon Scanning Centre of Excellence, part of the Foresight Directorate in the Department for Business, Innovation and Skills, has the role of supporting Departmental activities and facilitating cross-departmental collaboration.

Horizon Scanning, Tracking and Monitoring Processes

• Horizon Scanning, Tracking and Monitoring is a systematic search and examination of

global internet content – “BIG DATA” – information which is gathered, processed and

used to identify potential threats, risks, emerging issues and opportunities in the Human

World - allowing for the incorporation of mitigation and exploitation into in policy making

process - as well as improved preparation for contingency planning and disaster response.

• Horizon Scanning is used as an overall term for discovering and analysing the future of

the Human World – Politics, Economics, Sociology, Religion Culture and War –

considering how emerging trends and developments might potentially affect current policy

and practice. This helps policy makers in government to take a longer-term strategic

approach, and makes present policy more resilient to future uncertainty. In developing

policy, Horizon Scanning can help policy makers to develop new insights and to think

about “outside of the box” solutions to human threats – and opportunities.

• In contingency planning and disaster response, Horizon Scanning helps to manage risk

by discovering and planning ahead for the emergence of unlikely, but potentially high

impact Black Swan events. There are a range of Futures Studies philosophical

paradigms, and technological approaches – which are all supported by numerous

methods, tools and techniques for developing and analysing possible, probable and

alternative future scenarios.


• Scenario Planning and Impact Analysis is the archetypical method for futures studies

because it embodies the central principles of the discipline:

– The future is uncertain - so we must prepare for a wide range of possible, probable

and alternative futures, not just the future that we desire, or hope, will happen.....

– At the same time, it is vitally important that we think deeply and creatively about the

future, or else we run the risk of being either unprepared or surprised – or both.....

• Scenarios contain the stories of these multiple futures - from the hoped for to the expected

and from the wild-card to the Black Swan - in forms which are analytically coherent and

imaginatively engaging. A good scenario grabs our attention and says, ‘‘Take a good look

at this future. This could be your future - are you prepared ?’’

• As consultants and organizations have come to recognize the value of scenarios, they

have also latched onto one scenario technique – a very good one in fact – as the default

for all their scenario work. That technique is the Royal Dutch Shell / Global Business

Network (GBN) matrix approach, created by Pierre Wack in the 1970s and popularized by

Schwartz (1991) in the Art of the Long View and Van der Heijden (1996) in Scenarios: The

Art of Strategic Conversations. In fact, Millett (2003, p. 18) calls it the ‘‘gold standard of

corporate scenario generation.’’


• Scenarios are specially constructed stories about the future - each one portraying

a distinct, challenging and plausible world in which we might one day live and work - and for which we need to anticipate, plan and prepare.

• The Outsights Technique emphasises collaborative scenario building with internal clients and stakeholders. Embedding a new way of thinking about the future in the organisation is essential if full value is to be achieved – a fundamental principle of the “enabling, not dictating” approach

• The Outsights Technique promotes the development and execution of purposeful action plans so that the valuable learning experience from “outside-in” scenario planning enables building profitable business change.

• The Outsights Technique develops scenarios at the geographical level; at the business segment, unit and product level, and for specific threats, risks and challenges facing organisations. Scenarios add value to organisations in many ways: - future management, business strategy, managing change, managing risk and communicating strategy initiatives throughout an organisation.


1. War, terrorism and insecurity 2. Layers of power 3. Economic and financial stability 4. BRICs and emerging powers • Brazil • Russia • India • China

5. The Five Flows of Globalisation • Ideas • Goods • People • Capital • Services

6. Intellectual Property and Knowledge 7. Health, Wealth and Wellbeing 8. Transhumanism – Geo-demographics,

Ethnographics and Social Anthropology 9. Population Drift, Migration and Mobility 10. Market Sentiment, Trust and Reputation 11. Human Morals, Ethics, Values and Beliefs

12. History, Culture, Religion and Human Identity 13. Consumerism and the rise of the Middle Classes 14. Social Media, Networks and Connectivity 15. Space - the final frontier

• The Cosmology Revolution - String Theory

16. Science and Technology Futures • The Nano Revolution • The Quantum Revolution • The Information Revolution • The Bio-Technology Revolution • The Energy Revolution • Oil Shale Fracking • • Kerogen • Tar Sands • Methane Hydrate • • The Hydrogen Economy • Nuclear Fusion •

17. Science and Society – the Social Impact of Disruptive Technology and Convergence

18. Natural Resources – availability, scarcity and control – Food, Energy and Water (FEW) crisis

19. Climate Change • Global Massive Change – the Climate Revolution

20. Environmental Degradation & Mass Extinction 21. Urbanisation and the Smart Cities of the Future


• Outsights Strategy Scenarios create a shared context, clarity and vision over challenging issues shaping the future in which decision makers can take better informed decisions on opportunity exploitation and risk management strategies.

• Managing Change Scenario thinking can compel a wide range of people to open up to new options and change their own images of reality by sharing and discussing assumptions on what is shaping the world.

• The Outsights Technique translates what is learnt into action in the following ways to achieve sustainable change and risk management : -

– Providing the content and insight needed to understand changes in the outside world (Drivers of Change, Scenario Building, Risk Categories)

– Designing and executing processes to devolve organisational change, business transformation and risk management down from the segment and business unit level to the individual responsible manager level – delivering personal accountability for Strategy & Planning, Budgeting & Forecasting, Change Management, Risk Management, Performance Management and Standards Compliance with Enterprise Governance, Reporting and Controls


• Outsights Strategy Scenarios supports a shared resource pool covering those issues shaping the future in which decision makers can make difficult choices about opportunity exploitation and risk management strategies.

• The Outsights Technique helps stakeholders stand back, take stock and seek fresh points of view: -

– Facilitation of the internal debate exploring stakeholder value, opportunity exploitation and risk management

– Sounding board for business innovation and strategy

– Stakeholder engagement and the communication of the process with the wider partner, stakeholder and employee community

– Review of specific opportunity exploitation and risk management agendas

– Surfacing diverse opinions from internal and external stakeholders to identify needs for strategic content, clarity, perspective and action


• The insights discovered by Scenario Planning and Impact Analysis can provide the basis

for prioritising research and development programmes, gathering business intelligence,

designing organisational scorecard objectives and establishing visions and strategies.

Steps

1. Participants are given a scope, focus and time horizon for the exercise.

2. Horizon Scanning, Monitoring and Tracking and Monte Carlo Simulations provide

sources of information. These data sets can come from internal or external sources

– Data Scientists, Domain Experts and Researchers, “Big Data” Analysts, the project

team, or from prior studies and data collection exercises from the individual team

participants. These should cover a broad external analysis, such as STEEP.

3. Individuals review the sources and spot items that cause personal insights on the

focus given. These insights and their sources are captured in the form of abstracts.

4. Abstracts are discussed and themed to indicate wave-forms over the time horizon

concerned. Scenarios are stacked, racked and prioritised by impact and probability.

5. The participants agree on how to address the resulting Scenarios, Waves, Cycles,

Patterns and Trends with supporting information for further futures analysis.

• More information about tools and uses of horizon scanning in Central Government can be

found on the Foresight Horizon Scanning Centre website.

http://hsctoolkit.bis.gov.uk/STEEP-a.k.a.-PEST.html

http://hsctoolkit.bis.gov.uk/Review-of-abstracts.html

http://www.bis.gov.uk/foresight/our-work/horizon-scanning-centre


• THE THREE HORIZONS MODEL describes a Strategic Foresight method called “Seeing in Multiple Horizons: - Connecting Strategy to the Futures " The current THREE HORIZONS MODEL differs significantly from the original version first described in management literature over a decade ago. This model enables a range of Futures Studies techniques to be integrated with Strategy Analysis methods in order to reveal powerful and compelling future insights – and may be deployed in various combinations, whenever or wherever the Futures Studies techniques and Strategy Analysis methods are deemed to support the futures domains, subjects, applications and data in the current study.

• THE THREE HORIZONS MODEL method connects the Present Timeline with deterministic (desired or proposed) futures, and also helps us to identify probabilistic (forecast or predicted) future scenarios which may emerge as a result of interaction between embedded present-day factors and emerging catalysts of change – thus presenting us with a range of divergent possible futures. The “Three Horizons” method connects to models of change developed within the “Social Shaping” Strategy Development Framework via the Action Link to Strategy Execution. Finally, it summarises a number of futures applications where this evolving technique has been successfully deployed.

• The new approach to “Seeing in Multiple Horizons: - Connecting Strategy to the Future” has several unique features. It can relate change drivers and trends-based futures analysis to emerging issues. It enables policy or strategy implications of futures to be identified – and links futures work to processes of change. In doing so this enables Foresight to be connected to existing and proposed underlying system domains and data structures, with different rates of change propagation impacting across different parts of the system, and also to integrate seamlessly with tools and processes which facilitate Strategic Analysis. This approach is especially helpful where there are complex transformations which are likely to be radically disruptive in nature - rather than simple incremental transitions.

Andrew Curry Henley Centre HeadlightVision

United Kingdom

Anthony Hodgson Decision Integrity United Kingdom


The Three Horizons

Horizon and Environment Scanning, Tracking and Monitoring Processes

• Horizon and Environment Scanning, Tracking and Monitoring processes exploit the

presence and properties of Weak Signals – their discovery, analysis and interpretation

were first described by Stephen Aguilar Milllan in the 1960’s, and later popularised by

Ansoff in the 1970’s. Horizon Scanning is defined as “a set of information discovery

processes which data scientists, environment scanners, researchers and analysts use

to prospect, discover and mine the truly massive amounts of internet global content -

innumerable news and data feeds - along with the vast quantities of information stored

in public and private document libraries, archives and databases.”

• All of this external data is found widely distributed across the internet as Global Content

– RSS News Feeds and Data Streams, Academic Research Papers and Datasets - is

processed in order to detect and identify the possibility of unfolding random events and

clusters – “to systematically reduce the level of exposure to uncertainty, to reduce risk

and gain future insights in order to prepare for adverse future conditions – or to exploit

novel and unexpected opportunities for innovation" (LESCA, 1994). As a management

support tool for strategic decision-making, horizon and environment scanning process

have some very special challenges that need to be taken into account by environment /

horizon scanners, researchers, data scientists and analysts - as well as stakeholders.


• Horizon Scanning (Human Activity Phenomena) and Environment Scanning (Natural

Phenomena) are the broad processes of capturing input data to drive futures projects and

programmes - but they also refer to specific futures studies tool sets, as described below.

• Horizon Scanning, Tracking and Monitoring is a highly structured evidence-gathering

process which engages participants by asking them to consider a broad range of input

information sources and data sets - typically outside the scope of their specific expertise.

This may be summarised as looking back for historic Wave-forms which may extend into

the future (back-casting), looking further ahead than normal strategic timescales for wave,

cycle, pattern and trend extrapolations (forecasting), and looking wider across and beyond

the usual strategic resources (cross-casting). A STEEP structure, or variant, is often used.

• Individuals use sources to draw insights and create abstracts of the source, then share

these with other participants. Horizon scanning lays a platform for further futures activities

such as scenarios or roadmaps. This builds strategic analysis capabilities and informs

strategy development priorities. Once uncovered, such insights can be themed as key

trends, assessed as drivers or used as contextual information within a scenario narrative.

• The graphic image below illustrates how horizon scanning is useful in driving Strategy

Analysis and Development: -

http://hsctoolkit.bis.gov.uk/STEEP-a.k.a.-PEST.html

http://hsctoolkit.bis.gov.uk/Scenarios.html

http://hsctoolkit.bis.gov.uk/Roadmaps.html

Strategy versus Horizon Scanning


• Horizon Scanning, Tracking and Monitoring is the major input for unstructured “Big Data” to

be introduced into the Scenario Planning and Impact Analysis process (along with Monte

Carlo Simulation and other probabilistic models providing structured data inputs). In this

regard, Scenario Planning and Impact Analysis helps to create a conducive team working

environment. It allows consideration of a broad spectrum of input data – beyond the usual

timescales and sources – drawing information together in order to identify future challenges,

opportunities and trends. It looks for evidence at the margins of current thinking as well as in

more established trends. This allows the collective insights of the group to be integrated -

demonstrating the many differing ways which diverse sources contribute to these insights.

• Horizon Scanning, Tracking and Monitoring is ideal as an initial activity for collecting Weak

Signal data input into the Horizon Scanning, Tracking and Monitoring process to kick-off

major futures studies projects and future management programmes. Scenario Planning and

Impact Analysis is also useful as a sense-making and interaction tool for an integrated

future-focused team. Horizon Scanning, Tracking and Monitoring combined with Scenario

Planning and Impact Analysis works best if people external to the organisation are included

in the team - and are encouraged to help bring together new and incisive perspectives.

• The graphic image below illustrates how horizon scanning is useful in spotting weak signals

that might be otherwise difficult to see – and so risk being overlooked: -

Seeing in Multiple Horizons

Horizon Scanning, Tracking and Monitoring Processes

• Horizon Scanning, Tracking and Monitoring is a systematic search and examination

of global internet content – “BIG DATA” – information which is gathered, processed and

used to identify potential threats, risks, emerging issues and opportunities as a result of

Human Activity - allowing for the incorporation of mitigation and exploitation themes into

in the policy making process – as well as improved preparation for business continuity,

contingency planning and disaster response, and enterprise risk management events.

• Horizon Scanning is used as an overall term for discovering and analysing the unfolding

future of the Human World – Politics, Economics, Sociology, Religion Culture and War –

considering how emerging trends and developments might potentially affect current policy

and practice. This helps policy makers in government to take a longer-term strategic

approach, and makes present policy more resilient to future uncertainty. In developing

policy, Horizon Scanning can help policy makers to develop new insights and to think

about “outside of the box” solutions to human activity threats – and opportunities.

• In contingency planning and disaster response, Horizon Scanning helps to manage risk

by discovering and planning ahead for the emergence of unlikely, but potentially high

impact Black Swan events. There is a wide range of Futures Studies philosophical

paradigms, and technology approaches – which are all supported by numerous methods,

tools and techniques for exploring possible, probable and alternative future scenarios.


• Horizon and Environment Scanning Event Types – refer to Weak Signals of any unforeseen,

sudden and extreme Global-level transformation or change Future Events in either the military,

political, social, economic or environmental landscape - having an inordinately low probability of

occurrence - coupled with an extraordinarily high impact when they do occur (Nassim Taleb).

• Horizon Scanning Event Types



– Political, Economic and Social Waves

– Religion, Culture and Human Identity Waves

– Art, Architecture, Design and Fashion Waves

– Global Conflict – War, Terrorism, and Insecurity Waves

• Environment Scanning Event Types

– Natural Disasters and Catastrophes

– Human Activity Impact on the Environment - Global Massive Change Events

• Weak Signals – are messages, subliminal temporal indicators of ideas, patterns, trends or

random events coming to meet us from the future – or signs of novel and emerging desires,

thoughts, ideas and influences which may interact with both current and pre-existing patterns

and trends to predicate impact or effect some change in our present or future environment.

“Big Data”

Normal, daily routine activities from our everyday life generates vast amounts of data. Who owns this data, who has access to it, and what

they can do with it - is largely unknown, undisclosed and un-policed.....

Little-by-little, more and more aspects of our daily life are being monitored - meaning intimate details of what we do, where we go, and

who we see is now watched and recorded

“Big Data”

“Big Data” Global Content Analysis

• “Big Data” refers to those aggregated datasets whose size and scope is beyond the capability of conventional transactional Database Management Systems and Enterprise Software Tools to capture, store, analyse and manage. This definition of “Big Data” is of necessity subjective and qualitative – “Big Data” is defined as a large collection of unstructured information, which, when initially captured, contains sparse or undiscovered internal references, links or data relationships.

• Data Set Mashing or “Big Data” Global Content Analysis – supports Strategic Foresight Techniques such as Horizon Scanning, Monitoring and Tracking by taking numerous, apparently un-related RSS and other Information Streams and Data Feeds, loading them into Very large Scale (VLS) DWH Structures and Unstructured Databases and Document Management Systems for interrogating using Data Mining and Real-time Analytics – that is, searching for and identifying possible signs of hidden data relationships (Facts/Events) – in order to discover and interpret previously unknown “Weak Signals” indicating emerging and developing Scenarios, Patterns and Trends - in turn predicating possible, probable and alternative transformations, catalysts and agents of change which may develop and unfold as future “Wild Card” or “Black Swan” events.

Data-driven v. Model-driven Domains Model-driven

Data-driven Rationalism


Reaction

Scepticism

Dogma

Enlightenment

Pragmatism

Realism

Social Sciences

Sociology

Economics





Life Sciences








Earth Sciences

Economic Analysis


Applied mathematics

Geography

Geology

Chemistry

Engineering


Archaeology

Palaeontology


Future Management




Astronomy

Cosmology

Relativity

Astrophysics

Astrology


Climate Change



Statistics

Strategic Foresight


Cluster Theory

Pure mathematics

Particle Physics

String Theory

Quantum Mechanics


Futures Studies


Wave-form Analytics in “Big Data”

• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave

frequency analysis in Physics – and is based on Time-frequency analysis – a

technique which exploits the wave frequency and time symmetry principle. This is

introduced here for the first time in the study of human activity waves, and in the

field of economic cycles business cycles, patterns and trends.












Wave-form Analytics in Natural Cycles

• Solar, Oceanic and Atmospheric Climate Forcing systems demonstrate Complex

Adaptive System (CAS) behaviour - behaviour of ecologies which are more similar to an

organism than that of random and chaotic “Stochastic” systems. The remarkable long-

term stability and sustainability of cyclic climatic systems contrasted with random and

chaotic short-term weather systems are demonstrated by the metronomic regularity of

climate pattern changes driven by Milankovich, the 1470-year Dansgaard-Oeschger and

Bond Cycles – regular and predictable Solar and Oceanic Forcing Climate Sub-systems.

Wave-form Analytics in Human Activity Cycles

• Economic systems also demonstrate Complex Adaptive System (CAS) behaviour -

more similar to an ecology than chaotic “Random” systems. The capacity of market

economies for cyclic “boom and bust” – financial crashes and recovery - can be seen

from the impact of Black Swan Events causing stock market crashes - such as the

failure of sovereign states (Portugal, Ireland, Greece, Iceland, Italy and Spain) and

market participants (Lehman Brothers) due to oil price shocks, money supply shocks

and credit crises. Surprising pattern changes occurred during wars, arm races, and

during the Reagan administration. Like microscopy for biology, non-stationary time

series analysis opens up a new space for business cycle studies and policy diagnostics.




in nature to biological behaviour in an organism than to Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable



shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards).

Unexpected and surprising Cycle Pattern changes have historically occurred

during regional and global conflicts being fuelled by technology innovation-driven

arms races - and also during US Republican administrations (Reagan and Bush -

why?). Just as advances in electron microscopy have revolutionised biology -

non-stationary time series wave-form analysis has opened up a new space for


Big Data Analytics Goes Big Time

Big Data Analytics Goes Big Time • Organizations around the globe and across

industries have learned that the smartest business decisions are based on fact, not gut feel. That means they're based on analysis of data, and it goes way beyond the historical information held in internal transaction systems. Internet click-streams, sensor data, log files, mobile data rich with geospatial information, and social-network comments are among the many forms of information now pushing information stores into the big-data league above 10 terabytes.

• Trouble is, conventional data warehousing deployments can't scale to crunch terabytes of data or support advanced in-database analytics. Over the last decade, massively parallel processing (MPP) platforms and column-store databases have started a revolution in data analysis. But technology keeps moving, and we're starting to see upgrades that are blurring the boundaries of known architectures. What's more, a whole movement has emerged around NoSQL (not only SQL) platforms that take on semi-structured and unstructured information.

This info-graph presents from 2011 to 2013 update on what's available, with options including ExtremeData xdb, EMC's Greenplum appliance, Hadoop and MapReduce, HP's recently acquired the Autonomy and Vertica platforms, IBM's separate DB2-based Smart Analytic System and Netezza offerings, and Microsoft's Parallel Data Warehouse. Smaller, niche database players include Infobright, Kognitio and ParAccel. Teradata reigns at the top of the market, picking off high-end defectors from industry giant Oracle. SAP's Sybase unit continues to evolve Sybase IQ, the original column-store database. In short, there's a platform for every scale level and analytic focus

“Big Data”

Normal, daily routine activities from our everyday life generates vast amounts of data. Who owns this data, who has access to it, and what they can do with it - is largely unknown, undisclosed and un-policed..... Little-by-little, more and more aspects of our daily life are being monitored - meaning intimate details of what we do, where we go, and who we see is now watched and recorded.

“Big Data” Definitions

The Emerging “Big Data” Stack

Targeting – Map / Reduce

Consume – End-User Data

Data Acquisition – High-Volume Data Flows

– Mobile Enterprise Platforms (MEAP’s)

Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica

Smart Devices Smart Apps Smart Grid

Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting

– Data Delivery and Consumption

News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM

– Data Discovery and Collection

– Analytics Engines - Hadoop

– Data Presentation and Display

Excel Web Mobile

– Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load

– Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast database replication

– Data Management Tools DataFlux Embarcadero Informatica Talend

– Info. Management Tools Business Objects Cognos Hyperion Microstrategy

Biolap Jedox Sagent Polaris

Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox

– Data Warehouse Appliances

Ab Initio Ascential Genio Orchestra

“Big Data” Applications • Science and Technology

– Pattern, Cycle and Trend Analysis

– Horizon Scanning, Monitoring and Tracking

– Weak Signals, Wild Cards, Black Swan Events

• Multi-channel Retail Analytics – Customer Profiling and Segmentation

– Human Behaviour / Predictive Analytics

• Global Internet Content Management

– Social Media Analytics

– Market Data Management

– Global Internet Content Management

• Smart Devices and Smart Apps

– Call Details Records

– Internet Content Browsing

– Media / Channel Selections

– Movies, Video Games and Playlists

• Broadband / Home Entertainment

– Call Details Records

– Internet Content Browsing

– Media / Channel Selections

– Movies, Video Games and Playlists

• Smart Metering / Home Energy

– Energy Consumption Details Records

• Civil and Military Intelligence Digital Battlefields of the Future – Data Gathering

Future Combat Systems - Intelligence Database

Person of Interest Database – Criminal Enterprise,

Political organisations and Terrorist Cell networks

Remote Warfare - Threat Viewing / Monitoring /

Identification / Tracking / Targeting / Elimination

HDCCTV Automatic Character/Facial Recognition

• Security Security Event Management - HDCCTV, Proximity

and Intrusion Detection, Motion and Fire Sensors

Emergency Incident Management - Response

Services Command, Control and Co-ordination

• Biomedical Data Streaming Care in the Community

Assisted Living at Home

Smart Hospitals and Clinics

• SCADA Operational Technology SCADA Remote Sensing, Monitoring and Control

Smart Grid Data (machine generated data)

Vehicle Telemetry Management

Intelligent Building Management

Smart Homes Automation

Exploitation – “Big Data”

• There has been much speculation about how industries will cash in on “Big Data” In a nutshell “Big Data” occurs in volumes or structures that exceeds the functionality / capacity of conventional hardware, database platforms and analytical tools.

• Social media and search are leading the way with big data applications. As “Big Data” tools and methods enter the mainstream we will see businesses make use of the "data exhaust" that today doesn't get exploited To put it bluntly, most companies are failing to leverage their data assets by failing to realise the benefits of the huge volumes of data they are already generating.

Big Data Partnership

Training - For more information on Big Data Partnership’s training offerings, please visit the Training page. Feel free to Contact Us directly to discuss your specific needs.

http://bigdatapartnership.com/our-services/training/

http://bigdatapartnership.com/contact-us/

Big Data Partnership 3D Approach

• Discovery - As enterprises move into this new age for data analytics, companies can often struggle to identify where in their large data architecture, big data software and techniques can be utilised. Big Data Partnership can help those organisations understand where those use cases are through short workshop engagements. These are typically 2-5 days long and will help not only identify where Big Data Analytics could help drive more customer insight and ROI but also educate on what tools are in the eco-system.

• Develop - Even with solid use cases and a good understanding of how Big Data software and techniques could help businesses, it is not always easy to prove the model and commit to the necessary investment to really make the positive transformation in an organisation. One way of doing this is to take a single use case and develop a Proof of Concept to prove the expected ROI and business benefit and also validate the technology. This level of engagement can typically be a month long and can help businesses not only take the big step towards big data but also help them understand whether the expected ROI is there.

• Deliver - Big Data Partnership are able to assist enterprises in fully realising their Big Data initiatives through offering fixed price and day-based consultancy to help deliver full data analytics projects. We understand that each customer has differing needs, therefore we tailor our approach specific to each client. Effective big data is not just about predetermined buckets or templates for business intelligence; it is about meaningful analysis and processing of information in a way that is highly relevant to the business. We have highly skilled Data Scientists as well as deep rooted Big Data Engineers who can help you fully make the most of your implementations and ensure success of your Big Data projects.

“Big Data”

• Put yourself in the Big Data driver’s seat.

• Today, companies are generating massive amounts of data—everything from web clicks, to customer transactions, to routine business events—and attempting to mine that data for trends that can inform better business decisions.

• Quantivo enables a new analytics experience that is bound only by imagination of the user - it’s a full stack for turning raw data into intelligence. The Quantivo platform features patented, pattern-based technology that efficiently integrates event data across multiple sources, in hours not weeks. Your query quest starts here.

“Big Data” Analytics

Quantivo sifts through mountains of data—and spots the patterns that matter.

• When faced with overwhelming amounts of data, looking for the big “aha” can be next to impossible. That is, unless you’ve got Quantivo on your side. Unlike the other vendors that overpromise and under-deliver, Quantivo hits the mark with pattern-based analytics that brings Big Data down to size by tracking relationships between attributes and ignoring redundancies. With easy-to-use tools, users can zero in on predictive and repeatable patterns and trends—without losing any of the original data. In addition, Quantivo pattern-based analytics: -

– Creates behavioural segments derived from a combination of contextually specific attributes and online/offline detailed event data

– Uncovers buried relationships that link attributes to behaviours

– Tracks how behaviors change over time—and identifies the trigger for these changes

– Helps you “learn what you don’t know” by intelligently auto-compiling lists of patterns existing in your data

“Big Data” – Analysing and Informing

• “Big Data” is now a torrent raging through every aspect of the global economy – both the public sector and private industry. Global enterprises generate enormous volumes of transactional data – capturing trillions of bytes of information from their extended supply chain – global markets, customers and suppliers – and from their own internal business operations.

– SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN?

– GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE?

– INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW and WHY?

– SERVICE LAYER – Real-time and Predictive Analytics – WHAT / WHEN NEXT ?

– COMMUNICATION LAYER – Mobile Enterprise Platforms

– INFRASTRUCTURE LAYER – Cloud Service Platforms

The Emerging “Big Data” Stack

Targeting – Map / Reduce

Consume – End-User Data

Data Acquisition – High-Volume Data Flows

– Mobile Enterprise Platforms (MEAP’s)

Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica

Smart Devices Smart Apps Smart Grid

Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting

– Data Delivery and Consumption

News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM

– Data Discovery and Collection

– Analytics Engines - Hadoop

– Data Presentation and Display

Excel Web Mobile

– Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load

– Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast database replication

– Data Management Tools DataFlux Embarcadero Informatica Talend

– Info. Management Tools Business Objects Cognos Hyperion Microstrategy

Biolap Jedox Sagent Polaris

Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox

– Data Warehouse Appliances

Ab Initio Ascential Genio Orchestra


• SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN? – Remote Sensing – Sensors, Monitors, Detectors, Smart Appliances / Devices

– Remote Viewing – Satellite. Airborne, Mobile and Fixed HDCCTV

– Remote Monitoring, Command and Control – SCADA

• GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE? – Person and Social Network Directories - Personal and Social Media Data

– Location and Property Gazetteers - Building Information Models (BIM)

– Mapping and Spatial Analysis – Landscape Imaging & mapping, Global Positioning (GPS) Data

– Temporal / Geospatial data feeds –Weather and Climate, Land Usage, Topology / Topography

• INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW and WHY? – Content – Structured and Unstructured Data and Content

– Information – Atomic Data, Aggregated, Ordered and Ranked Information

– Transactional Data Streams – Smart Devices, EPOS, Internet, Mobile Networks


• SERVICE LAYER – Real-time and Predictive Analytics – WHAT / WHEN NEXT? – Global Mapping and Spatial Analysis - GIS

– Service Aggregation, Intelligent Agents and Alerts

– Data Analysis, Data Mining and Statistical Analysis

– Optical and Wave-form Analysis and Recognition, Pattern and Trend Analysis an Extrapolation

• COMMUNICATION LAYER – Mobile Enterprise Platforms and the Smart Grid – Connectivity - Smart Devices, Smart Apps, Smart Grid

– Integration - Mobile Enterprise Application Platforms (MEAPs)

– Backbone – Wireless and Optical Next Generation Network (NGE) Architectures

• INFRASTRUCTURE LAYER – Cloud Service Platforms – Public, Mixed / Hybrid, Enterprise, Private, Secure and G-Cloud Cloud Models

– Infrastructure – Network, Storage and Servers

– Applications – COTS Software, Utilities, Enterprise Services

– Security – Principles, Policies, Users, Profiles and Directories, Data Protection

Mobile Enterprise (MEAPs)

What Google Searches about the Future tell us about the Present...

• Internet Research published recently demonstrates how Internet Searches about future topics have a significant link to the economic success of the native country of the Search Requester.

• Google (GOOG) search data have become a statistical gold mine for academics, scientists, and number crunchers, who have used it for everything from predicting flu outbreaks to determining to what extent racial prejudice robbed Barack Obama of otherwise certain votes.

• Two academics in the U.K., Warwick Business School associate professor Tobias Preis and Dr. Helen Susannah Moat of University College London, analyzed more than 45 billion Google searches performed during 2012 and calculated the national ratio between searches that included “2013” and those that included “2011” for the native country of the Search Requester,

• They found that countries where “Internet users … search for more information about the future tend to have a higher per-capita GDP,” says Preis, who created a stir in 2010 when he used a similar data-crunching approach to quantify and model stock price fluctuations of companies on the Standard & Poor’s 500 index. “The more a country is looking to the future using Internet Searches, then the more successful economically the country is.”

• The rational is, when the economy is humming along nicely, it is easier to be optimistic—to plan vacations, buy season tickets, investigate investment opportunities, etc.

• Of all nations, the Germans are the most forward-looking, knocking Britons from the top spot. Preis explained that the U.K. scored so highly a year earlier because of the high national expectation around the forthcoming 2012 London Olympic Games. This year, the Germans are looking forward to a pivotal federal election. Preis, a German national, declined to comment on whether Germany’s exuberance for the future bodes well for incumbent Angela Merkel.

• Interestingly, the U.S. ranks 11th, up from 15th a year earlier. The 2012 findings showed that entering an election year, more Americans were looking backward to 2010. Preis says that this year, Americans as a whole are more optimistic about 2013 than they were a year earlier,.

• Economic laggards Pakistan, Vietnam, and Kazakhstan round out the bottom of the list.

http://investing.businessweek.com/research/stocks/snapshot/snapshot.asp?ticker=GOOG

http://www.businessweek.com/articles/2013-01-16/5-social-media-tools-to-fight-the-flu

http://campaignstops.blogs.nytimes.com/2012/06/09/how-racist-are-we-ask-google/

From sports to scientific research, a surprising range of industries will begin to find value in big data.....

Clustering in “Big Data” “A Cluster is a grouping of the same, similar and equivalent, data

elements containing values which are closely distributed – or

aggregated – together”

Clustering is a technique used to explore content and understand

information in every business and scientific field that collects and

processes verify large volumes of data

Clustering is an essential tool for any “Big Data” problem

• “Big Data” refers to vast aggregations (super sets) consisting of numerous individual

datasets (structured and unstructured) - whose size and scope is beyond the capability of

conventional transactional (OLTP) or analytics (OLAP) Database Management Systems

and Enterprise Software Tools to capture, store, analyse and manage. Examples of “Big

Data” include the vast and ever changing amounts of data generated in social networks

where we maintain Blogs and have conversations with each other, news data streams,





• Data Set Mashing and “Big Data” Global Content Analysis – drives Horizon Scanning,

Monitoring and Tracking processes by taking numerous, apparently un-related RSS and

other Information Streams and Data Feeds, loading them into Very large Scale (VLS)

DWH Structures and Document Management Systems for Real-time Analytics – searching

for and identifying possible signs of relationships hidden in data (Facts/Events)– in order to

discover and interpret previously unknown Data Relationships driven by hidden Clustering

Forces – revealed via “Weak Signals” indicating emerging and developing Application

Scenarios, Patterns and Trends - in turn predicating possible, probable and alternative

global transformations which may unfold as future “Wild Card” or “Black Swan” events.

“Big Data”

“Big Data”

Clustering Phenomena in “Big Data”

• The profiling and analysis of very large aggregated datasets in order to determine a

‘natural’ or implicit structure of data relationships or groupings – in order to discover

hidden data relationships driven by unknown factors where no prior assumptions

are made concerning the number or type of groups discovered or Cluster / Group

relationships, hierarchies or internal data structures – is a critically important starting

point – and forms the basis of many statistical and analytic applications.

• The subsequent explicit Cluster Analysis of discovered data relationships is an

important and critical technique which attempts to explain the nature, cause and

effect of unknown clustering forces driving implicit profile similarities, mathematical

or geographic distributions. Geo-demographic techniques are frequently used in

order to profile and segment Demographic and Spatial data by ‘natural’ groupings –

including common behavioural traits, Clinical Trial, Morbidity or Actuarial outcomes –

along with numerous other shared characteristics and common factors Cluster

Analysis attempt to understand and explain those natural group affinities and

geographical distributions using methods such as Causal Layer Analysis (CLA).....

Clustering in “Big Data”

Clustering in “Big Data”

“A Cluster is a group of profiled data similarities aggregated closely together”

• Clustering is an essential tool for any “Big Data” problem. Cluster Analysis of both

explicit (given) or implicit (discovered) data relationships in “Big Data” is a critical

technique which attempts to explain the nature, cause and effect of the forces which drive

clustering. Any observed profiled data similarities – geographic or temporal aggregations,

mathematical or statistical distributions – may be explained through Causal Layer Analysis.

• Cluster Analysis is a technique used to explore content and information in order to

understand very large volumes of data in every business and scientific field that collects

and processes vast quantities of machine generated (automatic) data

– Choice of clustering algorithm and parameters are processes and data dependent

– Approximate Kernel K-means provides a good trade-off between clustering accuracy

and data volumes, throughput, performance and scalability

– Challenges include homogeneous and heterogeneous data (structured versus

unstructured data), data quality, streaming, scalability, cluster cardinality and validity

Cluster Types Deep Space Galactic Clusters

Hadoop Cluster – “Big Data” Servers

Molecular Clusters

Geo-Demographic Clusters

Crystal Clusters

Cluster Types DISCIPLINE CLUSTER TYPE CLUSTERS DIMENSIONS DATA TYPE DATA SOURCE CLUSTERING

FACTORS / FORCES

Astrophysics Distribution of Matter through the Universe across Space and Time

Star Systems Stellar Clusters Galaxies Galactic Clusters

Mass / Energy Space / Time

Astronomy Images Optical Telescope Infrared Telescope Radio Telescope X-ray Telescope

Gravity Dark Matter Dark Energy

Climate Change Temperature Changes Precipitation Changes Ice-mass Changes

Hot / Cold Dry / Wet More / Less ice

Temperature Precipitation Sea / Land Ice

Average Temperature Average Precipitation Greenhouse Gases %

Weather Station Data Ice Core Data Tree-ring Data

Solar Forcing Oceanic Forcing Atmospheric Forcing

Actuarial Science Morbidity Epidemiology

Place / Date of birth Place / Date of death Cause of Death

Birth / Death Longevity Cause of Death

Medical Events Geography Time

Biomedical Data Demographic Data Geographic data

Register of Births Register of Deaths Medical Records

Health Wealth Demographics

Price Curves Economic Modelling Long-range Forecasting

Economic growth Economic recession

Bull markets Bear markets

Monetary Value Geography Time

Real (Austrian) GDP Foreign Exchange Rates Interest Rates Price movements Daily Closing Prices

Government Central Banks Money Markets Stock Exchange Commodity Exchange

Business Cycles Economic Trends Market Sentiment Fear and Greed Supply / Demand

Business Clusters Retail Parks Digital / Fin Tech Leisure / Tourism Creative / Academic

Retail Technology Resorts Arts / Sciences

Company / SIC Geography Time

Entrepreneurs Start-ups Mergers Acquisitions

Investors NGAs Government Academic Bodies

Capital / Finance Political policy Economic policy Social policy

Elite Team Sports Performance Science

Winners Loosens

Team / Athlete Sport / Club League Tables Medal Tables

Sporting Events Team / Athlete Sport / Club Geography Time

Performance Data Biomedical Data

Sports Governing Bodies RSS News Feeds Social Media Hawk-Eye Pro-Zone

Technique Application Form / Fitness Ability / Attitude Training / Coaching Speed / Endurance3

Future Management Human Activity Natural Events

Random Events Waves, Cycles, Patterns, Trends

Random Events Geography Time

Weak Signals Wild Card Events Black Swan Events

Global Internet Content / Big Data Analytics - Horizon Scanning, Tracking and Monitoring

Random Events Waves, Cycles, Patterns, Trends, Extrapolations

World-wide Visitor Count – GIS Mapping

Geo-Demographic Profile Data GEODEMOGRAPHIC INFORMATION – PEOPLE and PLACES

Age Dwelling Location / Postcode

Income Dwelling Owner / Occupier Status

Education Dwelling Number-of-rooms

Social Status Dwelling Type

Marital Status Financial Status

Gender / Sexual Preference Politically Active Indicator

Vulnerable / At Risk Indicator Security / Threat Indicator

Physical / Mental Health Status Security Vetting / Criminal Record Indicator

Immigration Status Profession / Occupation

Home / First language Professional Training / Qualifications

Race / ethnicity / country of origin Employment Status

Household structure and family members Employer SIC

Leisure Activities / Destinations Place of work / commuting journey

Mode of travel to / from Leisure Activities Mode of travel to / from work

BTSA Induction Cluster Map

Geo-Demographic Profile Clusters

Star Clusters

• New and

improved

understanding

of star cluster

physics brings

us within reach

of answering a

number of

fundamental

questions in

astrophysics,

ranging from

the formation

and evolution

of galaxies –

to intimate

details of the

star formation

process itself.

Hertzsprung Russell

• The Hertzsprung

Russell diagram is a

scatter plot Cluster

Diagram which shows

the Main Sequence

Stellar Lifecycles.

• A Hertzsprung Russell

diagram is a scatter

plot Stellar Cluster

Diagram which

demonstrates the

relationship between a

stars temperature and

luminosity over time –

using red to blue colour

to indicate the mean

temperature at the

surface of the star.

Star

Clusters • The Physics of star

clustering leads us

to new questions

related to the

make-up of stellar

clusters and

galaxies, stellar

populations in

different types of

galaxy, and the

relationships

between high-

stellar populations

and local clusters –

overall, resolved

and unresolved –

the implications

for their relative

formation times

and galactic star-

formation histories.

Cluster Analysis

• Data Representation – Metadata - identifying common Data Objects, Types and Formats

• Data Taxonomy and Classification – Similarity Matrix (labelled data)

– Grouping of explicit data relationships

• Data Audit - given any collection of labelled objects..... – Identifying relationships between discrete data items

– Identifying common data features - values and ranges

– Identifying unusual data features - outliers and exceptions

• Data Profiling and Clustering - given any collection of unlabeled objects..... – Pattern Matrix (unlabelled data)

– Discover implicit data relationships

– Find meaningful groupings (Clusters)

– Predictive Analytics – Event Forecasting

– Wave-form Analytics – Periodicity, Cycles and Trends

– Explore hidden relationships between discrete data features

Many big data problems feature unlabeled objects

Distributed Clustering Models

Number of processors

Speedup Factor - K-means

Speedup Factor - Kernel K-means

2 1.1 1.3

3 2.4 1.5

4 3.1 1.6

5 3.0 3.8

6 3.1 1.9

7 3.3 1.5

8 1.2 1.5

K-means

Kernel K -means

Clustering 100,000 2-D points with 2 clusters on 2.3 GHz quad-core

Intel Xeon processors, with 8GB memory in intel07 cluster

Network communication cost increases with the no. of processors

Cluster Analysis

Clustering Algorithms

Hundreds of spatial, mathematical and statistical clustering algorithms are available –

many clustering algorithms are “admissible” – but no single algorithm alone is “optimal”

• K-means

• Gaussian mixture models

• Kernel K-means

• Spectral Clustering

• Nearest neighbour

• Latent Dirichlet Allocation

Challenges in “Big Data” Clustering

• Data quality

• Volume – number of data items

• Cardinality – number of clusters

• Synergy – measures of similarity

• Values – outliers and exceptions

• Cluster accuracy - validity and verification

• Homogeneous versus heterogeneous data (structured and unstructured data)

k-means/Gaussian-Mixture Clustering of Audio Segments

Distributed Clustering Model Performance

Clustering 100,000 2-D points with 2 clusters on 2.3 GHz quad-core Intel Xeon processors, with 8GB memory in intel07 cluster Network communication cost increases with the no. of processors

K-means Kernel K -means

HPCC Clustering Models

High Performance / High Concurrence Real-time Delivery (HPCC)

Distributed Clustering Models

Distributed Clustering Model Performance

Distributed Approximate Kernel K-means

2-D data set with 2 concentric circles

2.3 GHz quad-core Intel Xeon processors, with 8GB memory in intel07 cluster

Run-time

Size of dataset (no. of Records)

Benchmark Performance (Speedup Factor )

10K 3.8

100K 4.8

1M 3.8

10M 6.4

Informatica / Hortonworks Vibe

Big Data – Products

The MapReduce technique has spilled over into many other disciplines that process vast

quantities of information including science, industry, and systems management. The Apache

Hadoop Library has become the most popular implementation of MapReduce – with

framework implementations from Cloudera, Hortonworks and MAPR

Split-Map-Shuffle-Reduce Process

Big Data Consumers

Split Map Shuffle Reduce

Key / Value Pairs Actionable Insights Data Provisioning Raw Data

Apache Hadoop Component Stack

HDFS

MapReduce

Pig

Zookeeper

Hive

HBase

Oozie

Mahoot

Hadoop Distributed File System (HDFS)

Scalable Data Applications Framework

Procedural Language – abstracts low-level MapReduce operators

High-reliability distributed cluster co-ordination

Structured Data Access Management

Hadoop Database Management System

Job Management and Data Flow Co-ordination

Scalable Knowledge-base Framework

Data Management Component Stack

Informatica

Drill

Millwheel

Informatica Big Data Edition / Vibe Data Stream

Data Analysis Framework

Data Analytics on-the-fly + Extract – Transform – Load Framework

Flume

Sqoop

Scribe

Extract – Transform - Load



Talend Extract – Transform - Load

Pentaho Extract – Transform – Load Framework + Data Reporting on-the-fly

Big Data Storage Platforms

Autonomy

Vertica

MongoDB

HP Unstructured Data DBMS

HP Columnar DBMS

High-availability DBMS

CouchDB Couchbase Database Server for Big Data with NoSQL / Hadoop

Integration

Pivotal Pivotal Big Data Suite – GreenPlum, GemFire, SQLFire, HAWQ

Cassandra Cassandra Distributed Database for Big Data with NoSQL and

Hadoop Integration

NoSQL NoSQL Database for Oracle, SQL/Server, Couchbase etc.

Riak Basho Technologies Riak Big Data DBMS with NoSQL / Hadoop

Integration

Big Data Analytics Engines and Appliances

Alpine

Karmasphere

Kognito

Alpine Data Studio - Advanced Big Data Analytics

Karmasphere Studio and Analyst – Hadoop Customer Analytics

Kognito In-memory Big Data Analytics MPP Platform

Skytree

Redis

Skytree Server Artificial Intelligence / Machine Learning Platform

Redis is an open source key-value database for AWS, Pivotal etc.

Teradata Teradata Appliance for Hadoop

Neo4j Crunchbase Neo4j - Graphical Database for Big Data

InfiniDB Columnar MPP open-source DB version hosted on GitHub

Big Data Analytics Engines / Appliances

Big Data Analytics and Visualisation Platforms

Tableaux Tableaux - Big Data Visualisation Engine

Eclipse Symentec Eclipse - Big Data Visualisation

Mathematica Mathematical Expressions and Algorithms

StatGraphics Statistical Expressions and Algorithms

FastStats Numerical computation, visualization and programming toolset

MatLab

R

Data Acquisition and Analysis Application Development Toolkit

“R” Statistical Programming / Algorithm Language

Revolution Revolution Analytics Framework and Library for “R”

Hadoop / Big Data Extended Infrastructure Stack

SSD Solid State Drive (SSD) – configured as cached memory / fast HDD

CUDA CUDA (Compute Unified Device Architecture)

GPGPU GPGPU (General Purpose Graphical Processing Unit Architecture)

IMDG IMDG (In-memory Data Grid – extended cached memory)

Vibe

Splunk

High Velocity / High Volume Machine / Automatic Data Streaming

High Velocity / High Volume Machine / Automatic Data Streaming

Ambari High-availability distributed cluster co-ordination

YARN Hadoop Resource Scheduling

Big Data Extended Architecture Stack

Cloud-based Big-Data-as-a-Service and Analytics

AWS Amazon Web Services (AWS) – Big Data-as-a-Service (BDaaS)

Elastic Compute Cloud (ECC) and Simple Storage Service (S3)

1010 Data Big Data Discovery, Visualisation and Sharing Cloud Platform

SAP HANA SAP HANA Cloud - In-memory Big Data Analytics Appliance

Azure Microsoft Azure Data-as-a-Service (DaaS) and Analytics

Anomaly 42 Anomaly 42 Smart-Data-as-a-Service (SDaaS) and Analytics

Workday Workday Big-Data-as-a-Service (BDaaS) and Analytics

Google Cloud Google Cloud Platform – Cloud Storage, Compute Platform,

Firebrand API Resource Framework

Apigee Apigee API Resource Framework

Gartner Magic Quadrant for BI and Analytics Platforms

Hadoop Framework Distributions

FEATURE Hortonworks Cloudera MAPR Pivotal

Open Source Hadoop Library Yes Yes Yes Pivotal HD

Support Yes Yes Yes Yes

Professional Services Yes Yes Yes Yes

Catalogue Extensions Yes Yes Yes Yes

Management Extensions Yes Yes Yes

Architecture Extensions Yes Yes

Infrastructure Extensions Yes Yes

Library

Support

Services

Catalogue

Job Management

Library

Support

Services

Catalogue

Hortonworks Cloudera MAPR

Library

Support

Services

Catalogue

Job Management

Resilience

High Availability

Performance

Pivotal

Library

Support

Services

Catalogue

Job Management

Resilience

High Availability

Performance

Gartner Magic Quadrant for BI

Data Warehouse Appliance / Real-time Analytics Engine Price Comparison

Manufacturer Server

Configuration Cached Memory

Server

Type

Software

Platform Cost (est.)

SAP HANA

(BI, BO, BW)

32-node (4

Channels x 8 CPU)

1.3 Terabytes

SMP Proprietary $ 6,000,000

Teradata 20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Proprietary $ 1,000,000

Netezza

(now IBM)

20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Proprietary $ 180,000

IBM ex5 (non-HANA

configuration)

32-node (4

Channels x 8 CPU)

1.3 Terabytes

SMP Proprietary $ 120,000

Greenplum (now

Pivotal)

20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Open Source $ 20,000

XtremeData xdb 20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Open Source $ 18,000

Zybert Gridbox 48-node (4

Channels x 12 CPU)

20 Terabytes

SMP Open Source $ 60,000


Slow is smooth, smooth is fast.....

.....advances in “Big Data” have lead to a revolution in

futures studies, forecasting and predictive modelling – but

it takes both human ingenuity, and time, for long-range

Models of the Future to develop and mature.....


• Abiliti: Origin Automation is part of a global consortium of Digital Technologies Service Providers and Future Management Strategy Consulting firms – Digital Marketing and Multi-channel Retail / Cloud Services / Mobile Devices / Big Data / Social Media

• Graham Harris Founder and MD @ Abiliti: Future Systems

– Email: [email protected] (Office) – Telephone: +44 (0) 121 445 4614 (Office)

• Nigel Tebbutt 奈杰尔泰巴德

– Future Business Models & Emerging Technologies @ Abiliti: Future Systems – Telephone: +44 (0) 7832 182595 (Mobile) – +44 (0) 121 445 5689 (Office) – Email: [email protected] (Private)

• Ifor Ffowcs-Williams CEO, Cluster Navigators Ltd & Author, “Cluster Development” – Address : Nelson 7010, New Zealand (Office)

– Email : [email protected]

Abiliti: Origin Automation Strategic Enterprise Management (SEM) Framework ©

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