Open System Thinking: at the crossroads of a complex worldFundación Sierra‐Pambley, León (Spain)

Information andInformation and Computation. Dynamics of ComplexityDynamics of Complexityas Natural ComputationGordana Dodig Crnkovic

Professor of Computer Science

Mälardalen University,Mälardalen University, 

School of Innovation, Design and Engineering

http://www.mrtc.mdh.se/~gdc/

Mälardalen Universityy

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My Teachingy g

CDT403 - Research Methods in Natural Sciences and EngineeringCDT403 - Research Methods in Natural Sciences and Engineering

CDT415 - Computing and Philosophy

DVA403 - Research Thinking and Writing ToolboxDVA403 - Research Thinking and Writing Toolbox

CDT314 - Formal Languages, Automata and Theory of Computation

CDT212 Information Kunskap Vetenskap (Information Knowledge Science)CDT212 - Information, Kunskap, Vetenskap (Information, Knowledge, Science)

CDT409 - Professional Ethics

http://www.idt.mdh.se/personal/gdc/work/courses.html

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My Research

Computing Paradigms, Natural/Unconventional Computing

Info-Computationalism, Computational aspects of Science of

f / f f S C )Information/Foundations of Information; Social Computing);

Computational knowledge generation, Computational aspects of

I lli d C i i Th f S i / Phil h f S iIntelligence and Cognition; Theory of Science/ Philosophy of Science;

Computing and Philosophy and

Ethics (Especially Ethics of Computing, Information Ethics, Roboethics and

Engineering Ethics).

http://www.idt.mdh.se/personal/gdc/work/publications.htmlp. 4

AbstractInformation is understood as representing the world (reality as an informational web) for a cognizingagent, while information dynamics (information processing, computation) realizes physical lawsthrough which all the changes of informational structures unfoldthrough which all the changes of informational structures unfold.

Processes considered rendering information dynamics have been studied, among others in:questions and answers, observations, communication, learning, belief revision, logical inference,game-theoretic interactions and computation. This lecture will put the computational approaches to

f f finformation dynamics into a broader context of natural computation, where information dynamics isnot only found in human communication and computational machinery but also in the entire nature.

Computation as it appears in the natural world is more general than the human process of calculationmodeled by the Turing machine Natural computing is epitomized through the interactions ofmodeled by the Turing machine. Natural computing is epitomized through the interactions ofconcurrent, in general asynchronous computational processes which are adequately represented bywhat Abramsky names “the second generation models of computation” which we argue to be themost general representation of information dynamics.

We will relate the layered organization of informational structures of complex systems find in naturaland social world to their dynamics understood as natural computation.

Keywords: information dynamics; natural computationalism; info-computationalism;unified theoriesKeywords: information dynamics; natural computationalism; info computationalism;unified theoriesof information; philosophy of information

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Based on the Articles

● Dodig-Crnkovic G., Dynamics of Information as Natural Computation, Information 2011, 2(3), 460-477; doi:10.3390/info2030460 Special issue: Selected Papers from FIS 2010 Beijing Conference, 2011. htt // d i /j l/i f ti / i l i / l t d b ijihttp://www.mdpi.com/journal/information/special_issues/selectedpap_beijinghttp://www.mdpi.com/2078-2489/2/3/460/ See also: http://livingbooksaboutlife.org/books/Energy_Connections

● Dodig-Crnkovic G. and Burgin M., Unconventional Algorithms: Complementarity of Axiomatics and Construction, Special issue of the journal Entropy, 14(11), 2066-2080; "Selected Papers from the Symposium on Natural/Unconventional Computing and its Philosophical Significance", doi:10.3390/e14112066 g ghttp://www.mdpi.com/journal/entropy/special_issues/unconvent_computing, 2012.

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Conceptual Basics: Computation as Information ProcessingComputation as Information Processing

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Conceptual Basics: Levels of Description – Levels of Abstraction –pLevels of Organization

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Conceptual Basics: Network Modellsp

Protein network in yeast cells Human protein interaction networkProtein network in yeast cells Human protein interaction network

Social networkHuman connectomep. 9

General Remark.Knowledge & Hinders to Knowledge

Knowledge – what we believe we know: (what we have strongKnowledge what we believe we know: (what we have strong reasons to believe we understand and hold for correct/true). “Philosophy: true, justified belief; certain understanding, as opposed to opinion.” http://oxforddictionaries.comto op o ttp //o o dd ct o a es co

Ignorance – white spots on the map of knowledge – what we know that we do not know.

False believes that look like knowledge – a part of our knowledge, what we believe we know, but actually we are wrong! False beliefs are much more dangerous and difficult epistemic problems than things that we understand that we do not know. Those are biggest hiders to new knowledge. Questioning of the “self-evident” is necessary, but self-evident is by its nature something we seldom suspect… (Fallibility of scientific knowledge – Popper) p. 10

Things That We Dont Know Well Enough

● COMPLEX SYSTEMS AND

EMERGENCY

PHYSICS AT VERY SMALL AND● PHYSICS AT VERY SMALL AND

VERY LARGE DIMENSIONS

● MECHANISMS OF LIFE &● MECHANISMS OF LIFE &

ORIGINS OF LIFE

● HUMAN BRAIN● HUMAN BRAIN

(including knowledge generation)

….p. 11

What We Miss in the Present Scientific Picture of the World

Specific questions:

● MECHANISTIC VS. COMPLEX

● REDUCTION VS. HOLISM (SYSTEM VIEW)

● OBSERVER DEPENDENCE VS. GOD EYE VIEW

● EMBODDIEDNESS OF ALL NATURAL PHENOMENA INCLUDING MIND

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Complexityp y

A complex system is a system composed of interconnectedparts that as a whole exhibit one or more propertiesparts that as a whole exhibit one or more properties(behavior among the possible properties) not obvious fromthe properties of the individual parts.[1]

A system’s complexity may be of one of two forms:disorganized complexity and organized complexity.[2]Disorganized complexity is a matter of a very large numberDisorganized complexity is a matter of a very large numberof parts, and organized complexity is a matter of the subjectsystem exhibiting emergent properties.http://en.wikipedia.org/wiki/Complex system

Complexity is found between orderly systems with highinformation compressibility and low information content andrandom systems with low compressibility and high

ttp //e ped a o g/ /Co p e _syste

random systems with low compressibility and highinformation content.

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Complexityp y

In a complex system what we see is dependent on where we are and whatIn a complex system, what we see is dependent on where we are and what

sort of interaction is used to study the system.

Study of complex systems:Generative Models

How does the complexity arize?Evolution is the most well known

ti h igenerative mechanism.

http://www.morphwize.com/company/index.php?option=com_k2&view=itemlist&task=tag&tag=complex+system+solutionp. 14

Complexity topics

Adaptation; Agent-based Modeling; Artificial Intelligence; Artificial Life; W. Ross Ashby; Biological Order and Levels of Organization;

p y p

; y; g g ;Cellular Automata; Chaos and Non-linear Dynamics; Cognitive Science; Collective Cognition; Complexity Measures; Cybernetics; Darwin Machines; Developmental Biology; Dissipative Structures; Edge of Chaos; Emergent Properties;Dissipative Structures; Edge of Chaos; Emergent Properties; Ergodic Theory; Evolution; Evolution of Complexity; Evolutionary Computation; Evolutionary Economics; Information Theory; Institutions and Organizations; Learning in Games; Machine g gLearning, Statistical inference and Induction; Multi-Agent Systems; Neural Nets, Connectionism, Perceptrons;Neuroscience; Nonequilibrium Statistical Mechanics and Thermodynamics; Pattern Formation; Physics of ComputationThermodynamics; Pattern Formation; Physics of Computation and Information; Physical Principles in Biology; Physics; Power Law Distributions and Long-Range Correlations; Ilya Prigogine; Random Boolean Networks, Self-organization; Simulations;

p. 15http://vserver1.cscs.lsa.umich.edu/~crshalizi/notabene/complexity.html

Complexity from Simplicityp y p yExamples

http://www youtube com/watch?v=gdQgoNitl1ghttp://www.youtube.com/watch?v=gdQgoNitl1gEmergence - Complexity from Simplicity, Order from Chaos (1 of 2)

(4.54)

http://www.youtube.com/watch?v=ONiWmzrmfuY&NR=1Murray Gell-Mann On Emergence (1.30)

htt // t b / t h? Lk6QU94 Ab8http://www.youtube.com/watch?v=Lk6QU94xAb8 Fractals - The Colors Of Infinity (53:45)

http://www.youtube.com/watch?v=u CaCie8R4U&feature=relatedhttp://www.youtube.com/watch?v u_CaCie8R4U&feature relatedPatterns in Nature (3.04)

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Generative Models:From the Origins up in ComplexityExamplesp

http://www.youtube.com/watch?v=U6QYDdgP9eg&NR=1The Origin of Life - Abiogenesis (10)The Origin of Life Abiogenesis (10)

http://www.youtube.com/watch?v=rtmbcfb_rdc&p=0696457CAFD6D7C9The Origin of the Genetic Code (9.37)

http://www.youtube.com/watch?v=6RbPQG9WTZM&feature=relatedThe Origin of the Brain (9.29)

http://www.youtube.com/watch?v=NEEXK3A57HkThe Origin of Intelligence (5.15)

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Self-OrganizationgExamples

http://www youtube com/watch?v=SkvpEfAPXn4&feature=fvwrelhttp://www.youtube.com/watch?v=SkvpEfAPXn4&feature=fvwrelRobots with a mind of their own (1.38)

http://www youtube com/watch?v=QdQBH 5Aabs&feature=relatedhttp://www.youtube.com/watch?v=QdQBH_5Aabs&feature=relatedSelf-replicating Kinematic Cellular Automata (0.06)

http://www youtube com/watch?v=KPP 4 LEHXQhttp://www.youtube.com/watch?v=KPP-4-LEHXQSelf Organization of vertical magnetic dipoles floating on water (2.53)

“SELF STAR” PROPERTIES IN ORGANIC SYSTEMS:“SELF-STAR” PROPERTIES IN ORGANIC SYSTEMS:self-organization, self-configuration (auto-configuration), self-optimisation(automated optimization), self-repair (self-healing), self-protection (automatedcomputer security), self-explaining, and self-awareness (context-awareness).computer security), self explaining, and self awareness (context awareness).

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Complex Adaptive Systemsp p yExamples

http://www.youtube.com/watch?v=TCOmBVrnDeA&feature=relatedComplex adaptive systems (1.02)

http://www.youtube.com/watch?v=bnKhzRpXPvM&feature=relatedA New Frontier: Systems Biology (12.55)

http://www.youtube.com/watch?v=KO7BIrHVQU0&playnext=1&list=PL04270148Fp y p yBDF86A6

Cities and Countries Part Two: Cultural Systems (4:45)

http://www.youtube.com/watch?v=t2JHbhYyoJcEunice E. Santos receives 2010 IEEE Computer Society Technical Achievement Award (1.38)

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Modelling of Complexityg p y

Li t d ibilit● Linear systems – decomposibility -Modelled by Analysis – Top-down – Global (Reductionism)

● Non-linear systems – behave as a whole –Modelled by Synthesis - Bottom-up, Distributed(Holism, System approaches)

● Complex behavior can emerge from SIMPLE GENERATORS!

http://www.youtube.com/watch?v=7WNUQspHyoAComplexity & Chaos. Emergence & Complexity (5.43)

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Agent-based Models g

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous individuals in a network, with a view to assessing their effects on the system as a whole.

It combines elements of game theory, complex systems, emergence, computational sociology, multi agent systems, and evolutionary programming.Monte Carlo Methods are used to introduce randomness.

The basic of ABMs the study of complexity and emergence.

http://www.youtube.com/watch?v=2C2h-vfdYxQ&feature=relatedComposite Agents (5.06)http://en wikipedia org/wiki/Agent based modelhttp://en.wikipedia.org/wiki/Agent-based_model

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Information

“The word “information” has been given different meanings byvarious writers in the general field of information theory It isvarious writers in the general field of information theory. It islikely that at least a number of these will prove sufficiently usefulin certain applications to deserve further study and permanentrecognition.It is hardly to be expected that a single concept of informationwould satisfactorily account for the numerous possibleapplications of this general field. “C Shannon, 1993, “The Lattice Theory of Information”

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Information

“Yet, despite its ubiquity in everyday experience, the concept ofinformation is conspicuously absent in the theories of classical physicsinformation is conspicuously absent in the theories of classical physics(such as classical mechanics and electromagnetism). The reason forthis is two-fold. First, in the classical framework, the universe evolvesdeterministically and reversibly - the past fully determines the futureand conversely the future determines the past so that no informationand, conversely, the future determines the past, so that no informationis lost or gained over the passage of time.Second, agents can always, in principle, perform measurements thatyield perfect and complete information about the state of reality.Th f t i i i l h th “G d' " i fTherefore, every agent in principle has the same “God's eye" view ofreality. (…)For these two reasons, the concept of information is essentiallyredundant in classical physics.”p y

Goyal P., Deciphering Quantum Theory. In "Are we there yet? The search for a theory of everything", Ed. M. Emam (Bentham Press, 2011)http://www.philipgoyal.org/resources/Papers/Introductory-Papers/DecipheringQT2010.pdf

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What is Information?

A special issue of the Journal of Logic, Language and Information (Volume 12 No 4 2003) is dedicated to theInformation (Volume 12 No 4 2003) is dedicated to the different facets of information.

A H db k h Phil h f I f i (VA Handbook on the Philosophy of Information (Van Benthem, Adriaans) is in preparation as one volume Handbook of the philosophy of science.

http://www.illc.uva.nl/HPI/

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Information as The Primary Stuff of ythe Universe - Paninformationalism

If information is to replace matter/energy as the primary stuff ofthe universe, as H von Baeyer (2003) suggests, it will provide anew basic unifying framework for describing and predictingnew basic unifying framework for describing and predictingreality in the twenty-first century.

L Floridi proposes Informational Structural Realism - a view ofp pthe world as the totality of informational objects dynamicallyinteracting with each other (Synthese Vol.161, Number 2, 219253, A defence of informational structural realism).

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Computation as Information pProcessing

With information as the primary stuff of the universe, the most general view of computation is as time-dependent behavior (dynamics) of information.(dynamics) of information.

This results in a Dual-aspect Universe: informational structure with computational dynamics (Info Computationalism Dodig Crnkovic)computational dynamics. (Info-Computationalism, Dodig Crnkovic)

Information and computation are closely related – no computation ith t i f ti ( t t ) d i f ti ith twithout information (structure), and no information without

computation (dynamics).

p. 26

Computation. Computing Nature and Nature Inspired ComputationNature Inspired Computation

If it looks like a duck, if it walks like a duck and it quacks like a duck, is it a duck? (If it looks like computation is it computation?)

http://www.bsse.ethz.ch/synbiohttp://synbio.mit.edu/research.html

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The Universe as a Computer -P t ti liPancomputationalism

We are all living inside a gigantic computer. No, not The Matrix: the Universe.

Every process, every change that takes place in the Universe, may be considered as a kind of computation.

K Zuse, N Wiener, E Fredkin, S Wolfram, G Chaitin, S Lloyd, G ‘t Hooft, C Seife, D Deutsch CF von Weizsäcker JA WheelerDeutsch, CF von Weizsäcker, JA Wheeler, among others

Simulation of the formation of cosmological structures

http://www.youtube.com/watch?feature=player_embedded&v=-ZcEDqyMbFwhttp://www.idt.mdh.se/personal/gdc/work/Pancomputationalism.mht

http://irfu.cea.fr/Sap/en/Phocea/Vie_des_labos/Ast/ast_sstheme.php?id_ast=1286

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Turing Machine Model of Computation

Tape............

Control UnitRead-Write head

Control Unit

1. Reads a symbol

W i b l2. Writes a symbol

3. Moves Left or Right

The definition of computation is currently under debate, and an entire issue of the journal Minds and Machines (1994, 4, 4) was devoted to the question “What is Computation?”Computation?

http://plato.stanford.edu/entries/turing-machine/ 29

Beyond Conventional Computing y p gMachinery: Natural Computing

● According to the Handbook of Natural Computingnatural computing is “the field of research thatinvestigates both human-designed computinginvestigates both human designed computinginspired by nature and computing taking place innature.”

● It includes among others areas of cellular automata● It includes among others areas of cellular automataand neural computation, evolutionary computation,molecular computation, quantum computation, nature-inspired algorithms and alternative models ofp gcomputation.

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Beyond Conventional Computing y p gMachinery: Natural Computing

● Denning argue that computing today is a naturalscience. Natural computation provides a basis for aunified understanding of phenomena of embodiedunified understanding of phenomena of embodiedcognition, intelligence and knowledge generation.

● ”I invite readers not on a visit to an archaeological● I invite readers not on a visit to an archaeological museum, but rather on an adventure in science in making” Prigogine in The End of Certainty: Time, Chaos and New Laws of Nature, 1997Chaos and New Laws of Nature, 1997

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Turing Machines Limitations –Self-generating Systems

According to George Kampis, http://hps.elte.hu/~gk/ complex

biological systems must be modeled as self-referential, self-

organizing systems called "component-systems" (self-

generating systems), whose behavior, though computational in

a generalized sense, goes far beyond Turing machine model.

p. 32

The Wildfire Spread of Computational p pIdeas

"Everyone knows that computational and information technology hasspread like wildfire throughout academic and intellectual life. But thespread of computational ideas has been just as impressive. Biologists notonly model life forms on computers; they treat the gene, and even wholeorganisms, as information systems. Philosophy, artificial intelligence, andcognitive science don't just construct computational models of mind; theyg j p ; ytake cognition to be computation, at the deepest levels. …Physicists don't just talk about the information carried by a subatomicparticle; they propose to unify the foundations of quantum mechanics withnotions of information “notions of information.

Brian Cantwell Smith, The Wildfire Spread of Computational Ideas, 2003

p. 33

Cognition as Computation (Processing of Information)(Processing of Information)Networks at the Basis of Cognition

100 billions of neurons connectedwith tiny "wires" in total longermore than two times the earthcircumference. This intricate andapparently messy neural circuitth t i ibl fthat is responsible for ourcognition and behavior.http://www.istc.cnr.it/group/locen

Biophysics of Computation: Information Processing in Single Neurons Christof Koch, 1999. http://www.klab.caltech.edu/~koch/biophysics-book/ p. 34

http://www.frontiersin.org/neuroscience/10.3389/fnins.2010.00200/fullhttp://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/

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What is Cognition?

● Process of perceiving

g

● Process of perceiving, reasoning, decision making and thinking.

● Body’s & brain ’s way of processing information to create meaning / makecreate meaning / make sense for an agent.

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Cognitive Network TechnologyCog t e et o ec o ogy

http://iptechwiki.cttc.es/Cognitive_Network_Optimization_Techniques 37

The Human Brain Project j(€500 million 10-year, FET flagship)

The human brain project has ten years to build the brain withThe human brain project has ten years to build the brain with supercomputers in collaboration between 80 research institutions in Europe. http://www.humanbrainproject.eu/

EPFL Lausanne, CHProf Henry MarkramProf. Henry Markram

28th January 2013

http://www.popsci.com/science/article/2013-02/how-simulate-human-brain-one-neuron-time-13-billion p. 38

Naturalized Epistemology Data – Information - KnowledgeData Information - Knowledge Information Processing

“Dynamics lead to statics, statics leads to dynamics, and thesimultaneous analysis of the two provides the beginning of anunderstanding of that mysterious process called mind ”understanding of that mysterious process called mind.

Goertzel, Chaotic Logic

Self-generating computation systems“a component system is a computer which, when executing its operations (software) builds a new hardware.... [W]e have a computer that re-wires itself in a hardware-software interplay: the hardware defines the software and the software definesthe hardware defines the software and the software defines new hardware. Then the circle starts again.”

George Kampis p 223 Self-Modifying Systems in Biology andGeorge Kampis, p. 223 Self-Modifying Systems in Biology and Cognitive Science

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Naturalized Epistemology Data – Information - Knowledge asData Information - Knowledge as Information Processing

Interactive explanation is future oriented, based on the fact that theagent is concerned with the anticipated future potentialities ofagent is concerned with the anticipated future potentialities ofinteraction.The actions are oriented internally to the system, which optimizestheir external outcome.their external outcome.The environment in the interactive case represents resources andconstraints for the agent.Information exchange with the environment is a part of interactiveInformation exchange with the environment is a part of interactiverelation.

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How Does Nature Compute? Evolution + Self-OrganizationEvolution + Self-Organization

C iti f th l ti h ti thCritics of the evolutionary approach mention the impossibility of “blind chance” to produce such highly complex structures as intelligent living organisms Proverbial monkeys typingorganisms. Proverbial monkeys typing Shakespeare are often used as illustration (an interesting account is given by Gell-Man in his Quark and the Jaguar )Quark and the Jaguar.)

Chaitin and Bennet: Typing monkeys’ argument does not take into account physical laws of the universe which dramatically limit what can beuniverse, which dramatically limit what can be typed. Moreover, the universe is not a typewriter, but a computer, so a monkey types random input into a computer The computer interprets theinto a computer. The computer interprets the strings as programs.

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Naturalizing Epistemology

Naturalized epistemology (Feldman, Kornblith, Stich) is, in general, an p gy ( , , ) , g ,idea that knowledge may be studied as a natural phenomenon --that the subject matter of epistemology is not our concept of knowledge, but the knowledge itself.

The stimulation of his sensory receptors is all the evidence anybody has had to go on, ultimately, in arriving at his picture of the world. Why

t j t h thi t ti ll d ? Wh t ttlnot just see how this construction really proceeds? Why not settle for psychology? ("Epistemology Naturalized", Quine 1969; emphasis mine)

I will re-phrase the question to be: Why not settle for computing?

Epistemology is the branch of philosophy that studies the nature, methods, limitations, and validity of knowledge and belief.

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Why not Settle for Computing h M d li C iti ?when Modeling Cognition?

In this framework knowledge is seen as a result of the structuring of input data:data → information → knowledgedata → information → knowledge by an interactive computational process going on in the nervous system during the adaptive interplay of an agent with the environment which clearly increases its ability to cope with theenvironment, which clearly increases its ability to cope with the dynamical changing of the world.

Hebbian learningHebbian learning http://en.wikipedia.org/wiki/Hebbian_theory

p. 43

Data – Information - Knowledge as gInformation Processing

Pragmatism suggests that interaction is the most appropriateframework for understanding of cognition.

Interactive explanation is future oriented; based on the fact that the agent is concerned with anticipated future potentialities of interaction.

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Data – Information - Knowledge as gInformation Processing

The actions are oriented internally to the system, which optimizes their internal outcome, while the environment in the interactive case represents primarily resources for the agent. Correspondence (mutual influence) with the environment is a part of interactive relation.

[One can say that living organisms are “about” the environment, that they have developed adaptive strategies to survive by internalizing environmental constraints. The interaction between an organism and its environment is realized through the exchange of physical signals that might be seen as data, or when structured, as information. Organizing and mutually relating different pieces of information results in knowledge. In that context, computationalism appears as the most suitable framework for naturalizing epistemology. ]

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Cognition as Computation

Information/computation mechanisms areInformation/computation mechanisms are fundamental for evolution of intelligent agents. Their role is to adapt the physical structure and behavior that will increase organisms chances of gsurvival, or otherwise induce some other behavior that might be a preference of an agent.

http://www.worldhealth.net/news/hormone-therapy-helps-improve-cognition

In this pragmatic framework, meaning in general is use, which is also the case with meaning of information.

http://www.ritholtz.com/blog/wp-content/uploads/2012/04/my-brain-hurts.png

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Naturalist Understanding ofUnderstanding of Cognition

A great conceptual advantage of cognition as a central focus of study is

that all living organisms possess some cognition, in some degree. g g p g , g

Maturana and Varela (1980)

Maturana’s and Varelas’ understanding of gcognition is most suitable as the basis for a computationalist account of the naturalized evolutionary epistemology.

See also: Gordana Dodig-CrnkovicWhere do New Ideas Come From? How do they Emerge? Epistemology as Computation (InformationEpistemology as Computation (Information Processing)in Randomness & Complexity, from Leibniz to Chaitin, C. Calude ed. 2007

p. 47

Unified Info-Computational N t li i f E i t lNaturalizing of Epistemology

At the physical level, living beings are open complex computational systemsin a regime on the edge of chaos, characterized by maximal informationalcontent.

Complexity is found between orderly systems with high informationcompressibility and low information content and random systems with low

ibilit d hi h i f ti t tcompressibility and high information content.

Fixed points are like crystals in that they are for the most part static andorderly Chaotic dynamics are similar to gases which can be described onlyorderly. Chaotic dynamics are similar to gases, which can be described onlystatistically. Periodic behavior is similar to a non-crystal solid, andcomplexity is like a liquid that is close to both the solid and the gaseousstates. In this way, we can once again view complexity and computation asexisting on the edge of chaos and simplicity. (Flake 1998)

p. 48

Unified Info-Computational N t li i f E i t lNaturalizing of EpistemologyDynamics of Open Systems

Based on the natural phenomena understood as info computationalBased on the natural phenomena understood as info-computational,computer in general is conceived as an open interactive system (digitalor analogue; discrete or continuous) in the communication with theenvironment.

Classical Turing machine is a subset of a more generalinteractive/adaptive/self-organizing universal natural computer. Living

fsystem is defined as "open, coherent, space- time structure maintainedfar from thermodynamic equilibrium by a flow of energy through it."(Chaisson, 2002.)

On a computationalist view, the dynamics of an organism areconstituted by computational processes. In the open system of livingcell an info-computational process takes place using DNA, exchangingp p p g g ginformation, matter and energy with the environment.

p. 49

Cognition as Re-structuring an A t th h I t ti ith

Info computationalist project of naturalizing epistemology by

Agent through Interaction with the Environment

Info-computationalist project of naturalizing epistemology bydefining cognition as information processing phenomenon isclosely related to the development of multilevel dynamicalcomputational models and simulations of cognizing systemscomputational models and simulations of cognizing systems,and has important consequences for the development ofartificial intelligence and artificial life.

Natural computation opens possibilities to implement embodiedcognition into artificial agents, and perform experiments onsimulated eco-systemssimulated eco systems.

p. 50

Unified Info-Computational Theory

Continuum-discrete controversy is bridged by the same dual-aspect approach (Info-Computationalism).

Thi t th t i t t ti l i d hi h l iThis counters the argument against computational mind which claims that computational mind must be discrete.

It is also an answer to the critique that the universe might not beIt is also an answer to the critique that the universe might not be computational as it might not be entirely digital.

Computation as information processing can be both continuous and discretediscrete.

p. 51

Unified Info-Computational Theory

Computationalist and informationalist frameworks meet in the commondomain of complex systemsdomain of complex systems.The Turing Machine model is about mechanical, syntactic symbolmanipulation as implemented on the hardware level.C l it i t b f d th ft l l Diff t l l fComplexity is to be found on the software level. Different levels ofcomplexity have different meanings for different cognizing agents.

p. 52

Computation in Closed vs. Open p pSystems

● Since the 1950s computational machinery has been● Since the 1950s computational machinery has beenincreasingly used to exchange information and computersgradually started to connect in networks and communicate. Inthe 1970s computers were connected via telecommunicationsthe 1970s computers were connected via telecommunications.

● The emergence of networking involved a rethinking of thenature of computation and boundaries of a computernature of computation and boundaries of a computer.Computer operating systems and applications were modified toaccess the resources of other computers in the network. In1991 CERN created the World Wide Web which resulted in1991 CERN created the World Wide Web, which resulted incomputer networking becoming a part of everyday life forcommon people.

p. 53

Computation in Closed vs. Open p pSystems

● With the development of computer networks two characteristics● With the development of computer networks, two characteristicsof computing systems have become increasingly important:parallelism/concurrency and openness – both based oncommunication between computational unitscommunication between computational units.

● Comparing new open-system with traditional closed-systemcomputation models Hewitt characterizes the Turing machinecomputation models, Hewitt characterizes the Turing machinemodel as an internal (individual) framework and his ownActor model of concurrent computation as an external(sociological) model of computing(sociological) model of computing.

p. 54

Computation as Interaction and pInteractive Computing

The following characteristics distinguish this new interactiveThe following characteristics distinguish this new, interactivenotion of computation (Wegner, Goldin):

C t ti l bl i d fi d f i t k [i● Computational problem is defined as performing a task, [in adynamical environment – my addition] rather than(algorithmically) producing an answer to a question.

● Dynamic input and output are modelled by dynamic streamswhich are interleaved; later values of the input stream mayd d li l i th t t t d idepend on earlier values in the output stream and vice versa.

p. 55

Computation as Interaction and pInteractive Computing

● The environment of the computation is a part of the model, playing an active role in the computation by dynamically supplying the computational system with the inputs, andsupplying the computational system with the inputs, and consuming the output values from the system.

● Concurrency: the computing system (agent) computes in parallel with its environment, and with other agents. (Agents canparallel with its environment, and with other agents. (Agents can consist of agents networks, recursively.)

● Effective non-computability: the environment cannot be assumed to be static or effectively computable. We cannotassumed to be static or effectively computable. We cannot always pre-compute input values or predict the effect of the system's output on the environment.

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Concurrencyy

The advantages of concurrency theory that is used to simulateobservable natural phenomena are that:

“it is possible to express much richer notions of time and spacein the concurrent interactive framework than in a sequentialone. In the case of time, for example, instead of a unique totalorder, we now have interplay between many partial orders ofevents--the local times of concurrent agents--with potentialsynchronizations, and the possibility to add global constraintson the set of possible scheduling. This requires a much morecomplex algebraic structure of representation if one wants tocomplex algebraic structure of representation if one wants to"situate" a given agent in time, i.e., relatively to the occurrenceof events originated by herself or by other agents.“

V Schachter “How Does Concurrency Extend the Paradigm ofV. Schachter, How Does Concurrency Extend the Paradigm ofComputation?,” Monist, vol. 82, no. 1, pp. 37–58, 1999.

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Digital vs. Analog, Discrete vs. g g,Continuous and Symbolic vs. Sub-symbolic Computationy p

● “Symbolic simulation is thus a two-stage affair: first the i f i f t t f th th t h dmapping of inference structure of the theory onto hardware

states which defines symbolic computation; second, the mapping of inference structure of the theory onto hardware states which (under appropriate conditions) qualifies thestates which (under appropriate conditions) qualifies the processing as a symbolic simulation.

● Analog simulation, in contrast, is defined by a single mapping from causal relations among elements of themapping from causal relations among elements of the simulation to causal relations among elements of the simulated phenomenon.”

R. Trenholme, “Analog Simulation,” Philosophy of Science, vol. 61, no. 1, pp. 115–131, 1994. p. 58

Symbolic vs. Sub-symbolic y yComputation

Douglas Hofstadter in his dialogue “Prelude…Ant fugue” in Godel, Escher, Bach. p. 59

The Unreasonable Ineffectiveness of Mathematics in Biology and Bias of Mathematicians

● “The unreasonable effectiveness of mathematics” as observed in physics by Wigner is missing in biologyobserved in physics by Wigner is missing in biology.

● Mathematics is disembodied* while computing is b di d i hi th t i t ith thembodied in a machine that can communicate with the

physical world in real time.

* Actually embodied in a human who takes the embodiment as grantedand disregards its constraints. p. 60

The Unreasonable Ineffectiveness Of Mathematics In Biology And Bias Of Mathematicians

Beyond the limits of TM model:● Embodiment invalidating the `machine as data' and universality

paradigm.● The organic linking of mechanics and emergent outcomes

delivering a clearer model of supervenience of mentality on brain functionality, and a reconciliation of different levels of effectivity.

● A reaffirmation of experiment and evolving hardware, for both AI and extended computing generally.

● The validating of a route to creation of new information through interaction and emergence.

S. B. Cooper, “Turing’s Titanic Machine?,” Communications ofthe ACM, vol. 55, no. 3, pp. 74–83, 2012p. 61

Info-computationalism, p ,in a Nutshell

● Nature can be described as a complex informationalstructure for a cognizing agent.

● Computation is information dynamics (information processing)

● Computation is constrained and governed by the lawsof physics on the fundamental level.p y

p. 62

Morphological Computing. p g p gMeaning Generation From Raw Data To Semantic Information

● Turing proposed diffusion reaction model of morphogenesis asthe explanation of the development of biological patterns such asthe spots and stripes on animal skin.

● Morphogenesis is a process of morphological computing.Physical process – though not computational in the traditionalsense, presents natural (unconventional), morphologicalcomputation. Essential element in this process is the interplaybetween the informational structure and the computational

i f ti lf t t i d i f ti i t tiprocess - information self-structuring and information integration,both synchronic and diachronic, going on in different time andspace scales in physical bodies.

p. 63

Morphological Computing. p g p gMeaning Generation From Raw Data To Semantic Information● Info-computational naturalism describes nature as

informational structure – a succession of levels ofinformational structure a succession of levels oforganization of information.

● Morphological computing on that informational● Morphological computing on that informationalstructure leads to new informational structures viaprocesses of self-organization of information.

● Evolution itself is a process of morphologicalcomputation on a long-term scale.

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Conclusions & Future Work

Present account highlights several topics ofPresent account highlights several topics ofimportance for the development of new understandingof computation and its role in the physical world:

● Natural computation● Natural computation● The relationship between model and physical

implementationI t ti it f d t l f t ti l● Interactivity as fundamental for computationalmodelling of concurrent information processingsystems such as living organisms and their networksN d l t i th ti l d lli d d● New developments in mathematical modelling neededto support this generalized framework.

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Conclusions & Future Work

● Besides the Turing machine as a well developed and● Besides the Turing machine as a well developed andgenerally established model of computation, variety ofnew ideas, still under development are taking shapeand have good prospects to extend our understandingand have good prospects to extend our understandingof computation and its relationship to physicalimplementations.

● It will be instructive within the info-computationalframework to study processes of self organization ofinformation in physical agents (as well as in populationinformation in physical agents (as well as in populationof agents) able to re-structure themselves throughinteractions with the environment as a result ofmorphological (morphogenetic) computation.p g ( p g ) p

p. 66

“(O)ur task is nothing less than to discover a new, ( ) g ,broader, notion of computation, and to understand the world around us in terms of information processing.”G. Rozenberg and L. Kari, “The many facets of natural computing,” g , y p g,Communications of the ACM, vol. 51, pp. 72–83, 2008.

p. 67

Qualitative Complexityp y

Ecology Cognitive Processes and the Re-Ecology, Cognitive Processes and the ReEmergence of Structures in Post-Humanist Social Theory

By John Smith, Chris Jenks

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Special Sciences andSpecial Sciences and the Unity of Science

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Books in the New Paradigm:A New Kind of Science

Book available at:http://www.wolframscience.com

Based on cellular automata, complexity emerging from repeating very simple rules

See alsohttp://www.youtube.com/watch?v=_eC14GonZnUA New Kind of Science - Stephen Wolfram

p. 70

Programming the Universe: A Quantum ComputerA Quantum Computer Scientist Takes on the CosmosCosmos

b S th Ll dby Seth Lloyd

p. 71

The Universe as Quantum I f iInformation

p. 72

A New Paradigm of Computing I i C i– Interactive Computing

Interactive Computation: the New Paradigm Springer-Verlag in September 2006 g g

Dina Goldin, Scott Smolka, Peter Wegner, eds.

--------------------------------------Dina Goldin, Peter Wegner The Interactive Nature of Computing:Refuting the Strong Church - Turing ThesisRefuting the Strong Church - Turing Thesis Minds and MachinesVolume 18 , Issue 1 (March 2008) p 17 - 38http://www.cs.brown.edu/people/pw/strong-cct.pdf

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Self-modifying Systems in Bi l d C i i S iBiology and Cognitive Science

The theme of this book is the self-generation of information by the self-modification of ysystems. The author explains why biological and cognitive processes exhibit identity changes in the mathematical and logical sense This concept is the basis of a newsense. This concept is the basis of a new organizational principle which utilizes shifts of the internal semantic relations in systems. More about the book:ftp://wwwc3.lanl.gov/pub/users/joslyn/kamp_rev.pdf

p. 74

A Computable Universe

p. 75

Computation, Information, Cognitionp g

Computation, Information,

CognitionEditor(s): Gordana Dodig Crnkovic and

Information and ComputationEditor(s): Gordana Dodig Crnkovic and

Mark Burgin, World Scientific, 2011

Computing NatureEditor(s): Gordana Dodig Crnkovic and

Raffaela Giovagnoli Springer 2013Editor(s): Gordana Dodig Crnkovic and

Susan Stuart, Cambridge Scholars

Publishing, 2007

g , , Raffaela Giovagnoli, Springer, 2013

http://dx.doi.org/10.1007/978-3-642-37225-4p. 76