Tomáš Sabol Ekon ó mia – pohľady z interdisciplinárnych brehov
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Tomáš Sabol Ekon ó mia – pohľady z interdisciplinárnych brehov. [email protected]. Obsah. Non traditional economics Specifics of information & knowledge Complexity and Complex adaptive systems Intelligence, Reflexivity, Bounded rationality Everything 2.0 - PowerPoint PPT Presentation
Transcript of Tomáš Sabol Ekon ó mia – pohľady z interdisciplinárnych brehov
Tomáš Sabol
Ekonómia – pohľady z interdisciplinárnych brehov
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Obsah• Non traditional economics• Specifics of information & knowledge• Complexity and Complex adaptive systems• Intelligence, Reflexivity, Bounded
rationality• Everything 2.0• Inspiration coming from biology & evolution• Related concepts (chaos, graphs, Gödel, ...)
Najľahšie je rozprávať o tom, o čom vieme málo, resp. skoro nič.Počul som to od pár múdrych ľudí
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Naozaj?• Bertrand Russell dropped his interest in economics
after half a year's study because he thought it was too simple. Max Planck dropped his involvement with economics because he thought it was too difficult.
• In 1990 Colander and Klamer asked students how important having a "thorough knowledge of the economy" was to succeeding as a economist. 3% percent thought it very important, and 68% thought it unimportant. Important was: "Being smart in the sense of being good at problem solving" and "excellence in mathematics."
W. Brian Arthur: Cognition: The Black Box of Economics
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• I have always believed that the complexity of the problems facing mankind is growing faster than our ability to solve them.
Doug Engelbart
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Ako sa stať inovatívnym?What is it that enables Google to be so
innovative?• We tell our engineering population, which is
roughly 50% of all Googlers, to work 70% of their time on their core job, 20% of their time on projects that are loosely connected with their core job (so if they work on search, then maybe 20% of their time looking at the fringes of search), and 10% of their time thinking about whatever they want to think about. No meetings. No emails. Nothing. You spend a day a fortnight thinking big thoughts.
Vice-president of Google .
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• A separate branch of economic and financial analysis, which applies scientific research on human and social, cognitive and emotional factors to better understand economic decisions by consumers, borrowers, investors, and how they affect market prices, returns and the allocation of resources– Theoretical basis for technical analysis
• Concerned with the bounds of rationality
Behavioral economic & behavioral finance
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• Studies how information affects an economy and economic decisions
• Information (cf. knowledge) has special characteristics:– Easy to create, but hard to trust– Easy to spread, but hard to control– Influences many decisions– Non-exclusion - consuming information does not
exclude someone else from also consuming it (but artificial exclusion constructs)
– Almost zero marginal cost - once the first copy exists, it costs (almost) nothing to make a second copy
Information Economics (or Economics of Information)
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– Information market does not exhibit high degrees of transparency. That is, to evaluate the information the information must be known, so you have to invest in learning it to evaluate it
– Information asymmetry - study of decisions in transactions where one party has more or better information than the other, creates an imbalance in power in transactions
– The value of knowledge by its use (and enhancement by the users’ feedback) increases (the value of ‘traditional’ assets by their use decreases)
• This (compared with other types of goods) complicate bying/selling models & many standard economic theories, new valuation techniques needed etc.
Information Economics (or Economics of Information)
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• The economics are not of scarcity, but rather of abundance. Unlike most resources that become depleted when used, information and knowledge can be shared, and actually grow through application.
• The effect of location is either:– Diminished in some economic activities: using
appropriate technology & methods, virtual marketplaces & virtual organizations offer benefits of speed, agility, 24/7, global reach
– or, on the contrary, reinforced in some other economic fields, by the creation of business clusters around centres of knowledge – universities, research centres.
• Laws, barriers, taxes etc are difficult to apply solely on a national basis. Knowledge & information "leak" to where demand is highest and the barriers lowest.
• Knowledge enhanced products or services can command price premiums over comparable products with low embedded knowledge or knowledge intensity.
Knowledge Economy vs Traditional economy
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• Pricing and value depends heavily on context. The same information or knowledge can have vastly different value to different people, or even to the same person at different times.
• Knowledge when locked into systems or processes has higher inherent value than when it can "walk out of the door" in people's heads.– See “Tacit” versus “Codified” knowledge
• Human capital (competencies) - a key component of value, yet few companies report competency levels in annual reports (~ cf. company valuation)
• Communication - fundamental to knowledge flows. Social structures, cultural context and other factors influencing social relations are of fundamental importance
Knowledge Economy vs Traditional economy
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• Growth of knowledge - complex evolutionary process • Adam Smith, Austrian school of economists• Production and management of knowledge in the frame
of economic constraints • Knowledge-based economy = the use of knowledge
technologies (knowledge engineering, knowledge management) to produce economic benefits
• Rules & practices that determined success in industrial economy need rewriting in an interconnected, globalized economy where knowledge resources (know-how, expertise) are as critical as other economic resources– at the level of firms & industries in terms of knowledge management– at the level of public policy as knowledge policy
Knowledge = the most important asset
Economy of Knowledge
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• Products and services are created and value is added through social networks operating on large or global scales– New bussines models needed
• Economies of scale stem from the size of the network - not the enterprise
• open system is preferable to a closed system– see also Systems theory
• …
Network Economy
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• Interdisciplinary research field, applying theories and methods originally developed by physicists in order to solve problems in economics - including uncertainty, stochastic processes, nonlinear dynamics
• statistical finance - application to the financial markets• Many of the more interesting phenomena in financial
markets fundamentally depend on heterogeneous agents and far-from-equilibrium situations (cf. Ilya Prigogine)
• Physics models applied in economics: percolation models, chaotic models (developed to study cardiac arrest), models with self-organizing criticality, models developed for earthquake prediction, mathematical theory of complexity and information theory (Claude Shannon, Murray Gell-Mann, …)
Econophysics
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• Quantum economy - a quantum economic model of a finite economic system that consists of an economic subsystem with a certain number of buyers and sellers (economy agents) and its external environment (institutions) with certain interactions between economy agents, and between the economy agents and institutions
• Analogies between finance theory & diffusion theory (the Black-Scholes equation for option pricing is a diffusion-advection equation)
Econophysics
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• ~ Something with many parts in intricate arrangement • 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 regime
• Many definitions tend to assume that complexity expresses a condition of numerous elements in a system and numerous forms of relationships among the elements
• Intricate inter-relationships that arise from the interaction of agents, which are able to adapt in and evolve with a changing environment
• Complexity of a particular system is the degree of difficulty in predicting the properties of the system if the properties of the system’s parts are given
Complexity
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Complexity
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• Complexity includes ideas such as: complex adaptive systems, self-organisation, co-evolution, agent based computer models, chaos, bifurcations, networks, emergence, fractals
• Complex systems constructed so that they are on the boundary between order and chaos are those best able to adapt by mutation and selection. Chaos is a subset of complexity – an example: so called butterfly effect: the idea is that a butterfly in Rio can change the weather in Chicago. An infinitesimal change in initial conditions leads to divergent pathways in the evolution of the system. Complex systems have evolved which may have learned to balance divergence and convergence, so that they're poised between chaos and order
Complexity
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• A scientific field which studies the common properties of systems that are considered fundamentally complex
• A new approach to science that studies how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment
• A broad term encompassing a research approach to problems in economics, anthropology, artificial life, chemistry, computer science, evolutionary computation, earthquake prediction, meteorology, molecular biology, neuroscience, physics, psychology, sociology
Complex Systems
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• One of Hayek's main contributions to early complexity theory is his distinction between the human capacity to predict the behaviour of simple systems and its capacity to predict the behaviour of complex systems through modeling. He believed that economics and the sciences of complex phenomena in general, which in his view included biology, psychology, and so on, could not be modeled after the sciences that deal with essentially simple phenomena like physics.
Complex systems
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Complexity in pictures
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Complexity in pictures
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• A CAS is a complex, self-similar collection of interacting adaptive agents. The study of CAS focuses on complex, emergent and macroscopic properties of the system
• Examples of complex adaptive systems include the stock market, social insect and ant colonies, the biosphere and the ecosystem, the brain and the immune system, the cell and the developing embryo, manufacturing businesses and any human social group-based endeavour in a cultural and social system such as political parties or communities. There are close relationships between the field of CAS and artificial life. In both areas the principles emergence and self-organization are very important.
Complex Adaptive Systems (CAS)
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• A Complex Adaptive System (CAS) is a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it has to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment by many individual agents.
Complex Adaptive Systems
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• Economic system is dynamically complex if its deterministic endogenous processes do not lead it asymptotically to a fixed point, a limit cycle, or an explosion– Some might argue that endogenous limit cycles
also constitute complexity, but we shall view them as merely nearly complex
– All systems that fit this definition have some degree of nonlinearity within them, however limited or arbitrary
– Note: There are nonlinear systems that are not complex, such as a standard exponential growth model
Complexity in Economics
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Limit Cycle
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• Application of complexity science to the problems of economics
• One of the four C's of a new paradigm surfacing in the field of economics: Cybernetics, Catastrophe, Chaos, Complexity
• Rejects traditional assumptions that imply that the economy is a closed system that eventually reaches an equilibrium.
• Views economies as open complex adaptive systems with endogenous evolution
• Complexity economics rejects many aspects of traditional economic theory (mathematical models formulated in an analogy with early models of thermodynamics - based on the first law of thermodynamics, equilibrium)
Complexity Economics
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• Claims that traditional economic models never adapted to the latter discovery, remain incomplete models of reality, and that mainstream economists are yet to introduce information entropy to their models
• Information entropy ("information uncertainty“) - important concepts of organization and disorder, viewed as basic state parameters, in describing/simulating the evolution of complex (including economic) systems
• Economic systems - no more considered as "naturally" inclined to achieve equilibrium states. On the contrary, economic systems - like most complex and self-organized systems - are intrinsically evolutionary systems, which tend to develop, prevailingly toward levels of higher internal organization; though the possibility of involution processes - or even of catastrophic events - remains immanent
Complexity Economics
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• No Global Controller — no global entity controls interactions. Controls are provided by mechanisms of competition & coordination between agents. Economic actions are mediated by legal institutions, assigned roles, shifting associations. No universal competitor — a single agent that can exploit all opportunities in the economy
• Continual Adaptation — Behaviors, actions, strategies, and products are revised continually as the individual agents accumulate experience — the system constantly adapts.
• Cross-cutting Hierarchical Organization — economy has many levels of organization and interaction. Units at any given level behaviors, actions, strategies, products typically serve as "building blocks" for constructing units at the next higher level. The overall organization is more than hierarchical, with many sorts of tangling interactions (associations, channels of communication) across levels.
Complexity Economics (1/2)
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• Dispersed Interaction — What happens in the economy is determined by the interaction of many dispersed, possibly heterogeneous, agents acting in parallel. The action of any given agent depends upon the anticipated actions of a limited number of other agents and on the aggregate state these agents co-create.
• Perpetual Novelty Niches — These are continually created by new markets, new technologies, new behaviors, new institutions. The very act of filling a niche may provide new niches. The result is ongoing, perpetual novelty.
• Out-of-Equilibrium Dynamics — Because new niches, new potentials, new possibilities, are continually created, the economy operates far from any optimum or global equilibrium. Improvements are always possible and indeed occur regularly.
Complexity Economics (2/2)
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Complexity Economics (1/3)Complexity Economics
Traditional Economics
Agents Modelled individually; use inductive rules of thumb to make decisions; have incomplete information; are subject to errors and biases; learn to adapt over time; heterogeneous agents
Modelled collectively; use complex deductive calculations to make decisions; have complete information; make no errors and have no biases; have no need for learning or adaptation (are already perfect), mostly homogeneous agents
Networks Explicitly model bi-lateral interactions between individual agents; networks of relationships change over time
Assume agents only interact indirectly through market mechanisms (e.g. auctions)
Emergence No distinction between micro/macro economics; macro patterns are emergent result of micro level behaviours and interactions.
Micro-and macroeconomics remain separate disciplines
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Complexity Economics (2/3)Complexity Economics
Traditional Economics
Evolution The evolutionary process of differentiation, selection and amplification provides the system with novelty and is responsible for its growth in order and complexity
No mechanism for endogenously creating novelty, or growth in order and complexity
Technology
Technology fluid, endogenous to the system
Technology as given or selected on economic basis
Preferences
Formulation of preferences becomes central; individuals not necessarily selfish
Preferences given; Individuals selfish
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Complexity Economics (3/3)Complexity Economics
Traditional Economics
Origins from Physical Sciences
Based on Biology (structure, pattern, self-organized, life cycle)
Based on 19th century physics (equilibrium, stability, deterministic dynamics)
Elements Patterns and Possibilities
Price and Quantity
Complex systems emerge and maintain “on the edge of chaos” - the narrow domain between frozen constancy and chaotic turbulence
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• Connections: Evolutionary theory ↔ Economics
• One of the fundamental problems in economics - Bounded rationality:
• How can agents who are not infinitely rational and do not have infinite computational resources get along in their worlds?
• There is an optimizing principle about precisely how intelligent such agents ought to be. If they are either too intelligent or too stupid - the system does not evolve well.
Evolutionary theory & Economics
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• Heterodox school of economic thought that is inspired by evolutionary biology
• Much like mainstream economics, it stresses complex interdependencies, competition, growth, structural change, and resource constraints but differs in the approaches which are used to analyze these phenomena
• Paul Krugman: What Economists Can Learn From Evolutionary Theorists – Economics is the study of phenomena
that can be understood as emerging from the interactions among intelligent, self-interested individuals
Evolutionary Economy
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• Paul Krugman:1. Economics is about what individuals do2. The individuals are self-interested3. The individuals are intelligent4. We are concerned with the interaction of
such individuals – What’s different in comparison to the biological
evolution? (BTW there is some difference) • Evolutionary theory: about the interaction
of self-interested individuals
Evolutionary Economy
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• ACE simulates and models economies as evolving systems of autonomous interacting agents
• Growing Economies from the bottom-up• Multi-agent system - a system composed
of multiple interacting intelligent agents, can be used to solve problems, which are difficult or impossible for an individual agent or monolithic system to solve. – Examples: online trading, disaster response,
modelling social structures
Agent-based Computational Economics
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• Altreva Adaptive Modeler - Building agent-based market simulation models for price forecasting of real-world stocks and other securities. A software application for creating financial market simulation models for the purpose of forecasting prices of real world market traded stocks or other securities or assets.
Agent-based Computational Economics
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•Collaborate, compete and share•Collective behaviour of a population of
interacting individuals often exhibits surprising properties, which can hardly be anticipated on the basis of the micro-economic interaction
•Predictions of the game theory, however, are often contradicted by empirical and experimental research already in simple cases (the prisoner’s dilemma, the ultimatum game)
•Doubts of the game theory validity in cases where individuals face a more complex strategic problem, involving a large number of other agents, uncertainty and limited information
Economics with heterogenous interacting agents
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• Deals with processes that exhibit self-reinforcing causation, as opposed to standard neoclassical equilibrium economics
• Represented by modern researchers in the fields of evolutionary-institutional economics, Post Keynesian economics, Ecological Economics, development and growth economics
• "The Foundations of Non-Equilibrium Economics: The Principle of Circular Cumulative Causation" (2009), Routledge
Non-equilibrium Economy
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• Approach to the management of information that treats human attention as a scarce commodity, and applies economic theory to solve various information management problems
• Attention is focused mental engagement on a particular item of information. Items come into our awareness, we attend to a particular item, and then we decide whether to act (Davenport & Beck 2001)
• A wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it (Simon 1971)
• Social attention• Dedicating too much attention to social interactions
→ "social interaction overload"
Attention Economy
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• Coined by T. Davenport“Understanding and managing attention is now the single most important determinant of business success."
Thomas H. Davenport and John C. Beck, The Attention Economy: Understanding the New Currency of Business (Boston: Harvard Business School Press, 2001).
Attention Economy
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Attention Economy
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• A shared or group intelligence that emerges from the collaboration & competition of many individuals
• The capacity of human communities to evolve towards higher order complexity and harmony, through such innovation mechanisms as differentiation & integration, competition, collaboration
– Appears in a wide variety of forms of consensus decision making in bacteria, animals, humans, computer networks
– Networking enabled by Web 2.0 (Enterprise 2.0), …• cf. group think and individual cognitive bias• Collective Intelligence networks & Prediction Markets
– enormously efficient means to harbor and codify vast landscapes of information and bring them to bear on the most difficult problems - productivity, innovation and business performance
Collective Intelligence
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• SI describes collective behavior of decentralized, self-organized systems, natural or artificial. SI systems - typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples of SI; ant colonies, bird flocking, animal herding, bacterial growth, …
Swarm Intelligence (SI)
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• How Mass Collaboration Changes Everything, Don Tapscott and Anthony D. Williams
• Four ideas: Openness, Peering, Sharing, Acting Globally
• Instead of an organized business body brought into being specifically for a unique function, mass collaboration relies on free individual agents to come together and cooperate to improve a given operation or solve a problem (Crowdsourcing)
• Coase's Law: A firm will tend to expand until the cost of organizing an extra transaction within the firm become equal to the costs of carrying out the same transaction on the open market
• Inversion of Coase's Law: A firm will tend to expand until the cost of carrying out an extra transaction on the open market become equal to the costs of organizing the same transaction within the firm
Wikinomics
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• Neuroeconomics:– combines neuroscience, economics & psychology
to study how people make decisions– looks at the role of the brain when we evaluate
decisions, categorize risks and rewards, and interact with each other
• Service Economics• Service Oriented Economy
Other economics
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• „Pomocou tohto princípu sa dívam na trhy. ... To, že trhy sú flexibilné platí, ale len do istej miery. Neviditeľná ruka trhu nie je neomylná, nie vždy. ... Za normálnych okolností hľadia trhy na realitu s miernym podozrením. Ale občas sa stane, že nálady trhov sa šíria po špirále, bez sebakontroly, mohutnejú až do seba-deštrukčnej sily – vtedy nastávajú bubliny“.
• „Po prvé, finančné trhy nereflektujú presne existujúcu situáciu, poskytujú obraz, ktorý je istým spôsobom zaujatý (‘biased’) alebo skreslený. Po druhé, skreslené názory účastníkov trhu, ktoré sa prejavujú v trhových cenách, môžu za istých podmienok ovplyvniť fundamenty, ktoré by mali trhové ceny reflektovať ...”
Reflexivita (George Soros)
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• Trhy obyčajne samy korigujú svoje chyby, ale občas do nich prekĺzne nesprávne pochopenie/interpretácia, ktorá je schopná upevniť trend, ktorý už existuje v skutočnosti, a tým upevniť samu seba. Takéto sebaupevňujúce procesy môžu odtlačiť trhy ďaleko od stavu rovnováhy. ... (tento proces) môže pokračovať dovtedy, kým nesprávna interpretácia začne byť tak očividná, že jej nesprávnosť všetci pochopia ...
• Túto obojstrannú spätnú, cyklickú spätnú väzbu medzi trhom, cenami a podcenenou realitou volám reflexivita.“
• Reflexivity – circular, bidirectional relationships between cause and effect, both the cause and the effect affect one another in a situation that renders both functions causes and effects
• Observations or actions of observers in the social system affect the very situations they are observing
Reflexivita (George Soros)
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• Rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions
• Since decision-makers lack the ability and resources to arrive at the optimal solution, they apply their rationality only after having greatly simplified the choices available– Economic agents employ the use of heuristics to
make decisions rather than a strict rigid rule of optimization
– They do this because of the complexity of the situation, and their inability to process and compute the expected utility of every alternative action
– In contrast with the concept of rationality as optimization
Bounded Rationality (Herbert Simon)
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• Decision-maker is a satisficer (one seeking a satisfactory solution) rather than optimizer– Perfectly rational decisions are often not feasible
in practice due to the finite computational resources available for making them
– Most people are only partly rational, and are emotional/irrational in the remaining part of their actions
• Economics is also about scarcity of our computational resources!
Bounded rationality (Herbert Simon)
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• Systems theory - an interdisciplinary theory about the nature of complex systems, a framework by which one can investigate and/or describe any group of objects that work together to produce some result. First originated in biology in the 1920s out of the need to explain the interrelatedness of organisms in ecosystems (Ludwig von Bertalanffy)
• Scientific modelling is the process of generating abstract, conceptual, graphical and/or mathematical models
• Memetics - theoretical and empirical science that studies the replication, spread and evolution of memes
• Meme: an information pattern held in an individual's memory, which is capable of being copied to another individual's memory / a cognitive or behavioral pattern that can be transmitted from one individual to another one / contagious ideas, all competing for a share of our mind
Related topics
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• Chaos theory - an area of inquiry studying the behavior of dynamical systems that are highly sensitive to initial conditions - the butterfly effect - small differences in initial conditions yield widely diverging outcomes for chaotic systems, rendering long-term prediction impossible in general – Applied in biology, computer science, economics, finance,
physics, politics, population dynamics, psychology, ...• Second order cybernetics (cybernetics of cybernetics)
– Investigates cybernetics with awareness that the investigators are part of the system
– Importance of self-referentiality, self-organizing, the subject-object problem
– To construct a model of the mind a brain is required to write a theory of a brain
Related topics
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• Ilya Prigogine - dissipative structures and their role in thermodynamic systems far from equilibrium – Dissipative structures - led to pioneering
research in self-organizing systems, as well as philosophic inquiries into the formation of complexity on biological entities
– Organisms are unstable systems existing far from thermodynamic equilibrium, due to sensitivity to initial conditions, unstable systems can only be explained statistically
Related topics
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• Autopoesis - "auto (self)-creation" - expresses a fundamental dialectic between structure and function
• Introduced by Humberto Maturana and Francisco Varela• An autopoietic machine is a machine organized
(defined as a unity) as a network of processes of production (transformation and destruction) of components which: – i) through their interactions and transformations
continuously regenerate and realize the network of processes (relations) that produced them; and
– ii) constitute it (the machine) as a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network
Related topics
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• Dispersed knowledge (in economics) - information that is dispersed throughout the marketplace, and is not in the hands of any single agent. All agents in the market have imperfect knowledge; however, they all have a good indicator of everyone else's knowledge and intentions, and that is the price– Price signals are one possible solution to the
economic calculation problem. – popular especially among Austrian School
economists such as Friedrich Hayek
Related topics
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• Evolution of knowledge (cf. evolutionary epistemology) as a part of the cultural evolution can be modelled through the same basic principles of variation and selection that underly biological evolution. Shift from genes as units of biological information to memes (a new type of units of cultural information)→ Richard Dawkins
• A bifurcation occurs when a small smooth change made to the parameter values (the bifurcation parameters) of a system causes a sudden 'qualitative' or topological change in its behaviour
Related topics
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Bifurcation diagram
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• Crowdsourcing - the act of taking tasks traditionally performed by an employee or contractor, and outsourcing them to a group (crowd) of people or community in the form of an open call– Distributed problem-solving and production model (to
develop a new technology, carry out a design task etc.)• Social software - a range of SW systems that allow
users to interact and share data – MySpace, Facebook, Flickr, Linked-In, YouTube
• Enterprise social software (a major component of Enterprise 2.0), comprises social software as used in "enterprise" (business/commercial) contexts
• Enterprise 2.0 - provides business managers with access to the right information at the right time through a web of inter-connected applications, services and devices. Enterprise 2.0 makes accessible the collective intelligence of many, translating to a huge competitive advantage in the form of increased innovation, productivity and agility
Related topics
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Enterprise 1.0 vs. Enterprise 2.0 Enterprise 1.0 Enterprise 2.0Hierarchy Flat OrganizationFriction Ease of Organization
FlowBureaucracy AgilityInflexibility FlexibilityIT-driven technology / Lack of user control
User-driven technology
Top down Bottom upCentralized DistributedTeams are in one building / one time zone
Teams are global
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Enterprise 1.0 vs. Enterprise 2.0 Enterprise 1.0 Enterprise 2.0Silos and boundaries Fuzzy boundaries, open
bordersNeed to know TransparencyInformation systems are structured and dictated
Information systems are emergent
Taxonomies FolksonomiesOverly complex SimpleClosed/ proprietary standards
Open
Scheduled OpenLong time-to-market cycles
Short time-to-market cycles
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• Small World Network – graph in which most nodes are not neighbours of one another, but most nodes can be reached from every other by a small number of hops or steps– Small-world network characteristics: priemerná vzdialenosť medzi 2
uzlami siete – Internet (10), Web (19), social networks - ľudia (6, AKA six degrees of separation, Stanley Milgram), neuróny v mozgu (14), ...
• Bezškálová topológia s (niekoľkými) dominantnými centrami, t.j. „zdatní bohatnú“– mocninový zákon: N(k) = 1 / k
• k – konektivita uzla (počet spojení daného uzla)• N(k) – počet uzlov siete s konektivitou „k“ – exponent konektivity
• Štruktúra a vývoj sietí sa nedajú od seba oddeliť • Topológia vs. Stabilita a Robustnosť komplexných sietí - závisí
od
Related topics
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• Gödel's first incompleteness theorem - Any effectively generated theory, capable of expressing elementary arithmetic, cannot be both consistent and complete. In particular, for any consistent, effectively generated formal theory that proves certain basic arithmetic truths, there is an arithmetical statement that is true, but not provable in the theory.
• (Gödel's Second Incompleteness Theorem. In any consistent (axiomatizable) theory the consistency of the system is not provable in the system.)
• Alan Turing - undecidable problems (e.g. halting problem) no total computable function
Related topics
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• http://www.futurict.ethz.ch/– With our knowledge of the universe, we have sent men to the
moon. – We know microscopic details of objects around us and within us. – And yet we know relatively little about how our society works and
how it reacts to changes brought upon it.– Humankind is now facing serious crises for which we must develop
new ways to tackle the global challenges of humanity in the 21st century.
• It is thus timely to create an ICT Flagship to explore social life on Earth, and everything it relates to, in the same way that we have spent the last century or more understanding our physical world.
• This proposal sketches out visionary scientific endeavours, forming an ambitious concept that allows us to answer a whole range of challenging questions. Integrating the European engineering, natural, and social science communities ...
• Budget: 1 billion EUR
FuturICT Project
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• Develop novel ICT systems combining the best of human and computational abilities to support the understanding, integrative design, and management of complex systems,
• Apply these to model techno-social and economic, transport, environmental and other global systems,
• Create instruments to support the self-organization, decision-making and governance in politics, business, industry, and academia, with the aim to foster societal goals (e.g. robust techno-social and sustainable economic systems),
• Develop principles and tools facilitating emergence of high quality processes, products and institutions in techno-social networks.– Multi-agent simulations of large systems (e.g. “whole earth simulation”,
which may involve up to 10 billion agents)– Multi-agent simulations considering human cognitive & psychological
processes (e.g. personality, memory, strategic decision-making, emotions, creativity etc.) …
FuturICT Project – Goals:
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Realistic Theory of Economic Systems: • Develop a realistic economic theory to give more reliable advise to
decision-makers; • Go beyond the paradigms of the “Homo Economicus” (the “perfect
egoist”), efficient markets, equilibrium models, and representative agent models (mean-field models);
• Develop agent-based models of boundedly rational behavior (e.g. limited cognitive capacities, behavioral biases and emotional aspects);
• Consider randomness, extreme events, heterogeneity, non-linearity, emergence, and complexity to yield a greater descriptive and predictive model validity;
• Make models consistent with empirical and experimental data; • Work out the mathematical connection between microscopic and
macroscopic economic theories
FuturICT Project
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• Trend - narastanie zložitosti / networking – rastie počet prepojení Globalizácia, interdisciplinarita, deterministický chaos Existujú medze zložitosti? Narastá „gap“ medzi zložitým systémo a našim modelom
tohto systému?• Systémy obsahujúce kognitívne entity
Self-reference, self-reflective, self-adaptation, self-organisation, self-awareness, consciousness, self-reproduction, ...
Meta-modelling Reflexivity (Soros) Neúplnosť poznania (Popper, ...) Princíp neurčitosti v poznaní (nástroj vs predmet poznania)
• Hypotéza obmedzenej racionality (bounded rationality) Pracujeme s neúplnymi modelmi
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67
• Economy of intangibles • Špecifiká nehmotných aktív (informácie, znalosti, ...)
• Neúplnosť nášho poznania• Hypotéza obmedzenej racionality - H. Simon• Matematická neúplnosť systému – Godel, … • Neúplný model zložitého systému• Individual cognitive bias, group think
• Top-down vs. Bottom-up popisy/modely• Agent-based modelling, emergencia
• Ekonómia – veda o nerovnovážnych, dynamických systémoch• Complex adaptive system – M. Gell-Mann, J. Holland, S.
Kauffman• Je ekonomika systém, ktorý je nezávislý od našich predstáv,
očakávaní, modelov, teórií tohto systému?• self-reflektivita
Conclusions
68
• Siete • Hypotéza malého sveta• Bez-škálové grafy (scale-free graphs) – robustnosť
• Crowdsourcing, open innovation, ... - kedy je dav múdrejší ako jednotlivec a kedy je to viditeľne naopak?
• Je inteligencia balansovanie na hrane chaosu a usporiadanosti?
• Čo implikuje skutočnosť, že ekonómia je spoločenská veda?
• ...• V 60-tych rokoch 20. storočia veľa pojmov a inšpirácií
do spoločenských vied (psychológia, sociológia, ekonómia, ...) prichádzalo z fyzikálnych disciplín, v súčasnosti inšpirácia (resp. spôsob nazerania na systém) prichádza z biológie – pojmy ako ekológia, (ko-)evolúcia (ako gradualistický proces), ...
Conclusions
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• http://en.wikipedia.org/wiki/Information_economy• http://en.wikipedia.org/wiki/Complexity • http://www.complexity-society.com/ • Journal: Studies in Non-linear Dynamics & Econometrics,
http://www.bepress.com/snde/• http://www.econ.iastate.edu/tesfatsi/ace.htm• http://www.econ.iastate.edu/tesfatsi/ace.htm • http://en.wikipedia.org/wiki/Comparison_of_agent-based_
modeling_software• http://en.wikipedia.org/wiki/Adaptive_Modeler - SW for
creating financial market simulation models for the purpose of forecasting prices of real world market traded stocks, other securities, assets
• http://en.wikipedia.org/wiki/Complex_adaptive_system• http://en.wikipedia.org/wiki/Service_economy• http://pespmc1.vub.ac.be/
Zdroje
70
• Memetika, http://pespmc1.vub.ac.be/memes.html, http://www.memecentral.com/
• Bifurcation theory, http://en.wikipedia.org/wiki/Bifurcation_theory
• JAMEL (Java Agent-based MacroEconomic Laboratory) - building agent-based macroeconomic simulations
• JASA (Java Auction Simulator API) - Computational economics; Agent based computational economics
• http://en.wikipedia.org/wiki/Comparison_of_agent-based_modeling_software
• Growing Economies from the Bottom Up, http://www.econ.iastate.edu/tesfatsi/ace.htm
• http://en.wikipedia.org/wiki/Knowledge_economy• http://en.wikipedia.org/wiki/Network_Economy • Journal: Review of Network Economy,
http://www.bepress.com/rne/vol8/iss4/1/?sending=10825• http://en.wikipedia.org/wiki/Evolutionary_economics• Journal of Evolutionary Economics
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• http://en.wikipedia.org/wiki/Non-equilibrium_economics• http://en.wikipedia.org/wiki/Quantum_economy• http://en.wikipedia.org/wiki/Bounded_rationality • http://en.wikipedia.org/wiki/Herbert_Simon • http://en.wikipedia.org/wiki/Collective_intelligence • http://en.wikipedia.org/wiki/Groupthink • http://en.wikipedia.org/wiki/List_of_cognitive_biases • http://en.wikipedia.org/wiki/Neuroeconomics• http://en.wikipedia.org/wiki/Behavioral_economics• http://en.wikipedia.org/wiki/Wikinomics• http://en.wikipedia.org/wiki/Complexity_economics• http://en.wikipedia.org/wiki/Crowdsourcing• http://en.wikipedia.org/wiki/Social_software • http://en.wikipedia.org/wiki/Autopoiesis
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• http://www.e2conf.com/about/what-is-enterprise2.0.php • http://en.wikipedia.org/wiki/Crowdsourcing • http://en.wikipedia.org/wiki/Mathematical_model • http://en.wikipedia.org/wiki/Social_software • http://en.wikipedia.org/wiki/Multi-agent_system • http://en.wikipedia.org/wiki/Small-world_network • http://en.wikipedia.org/wiki/Prediction_markets• http://en.wikipedia.org/wiki/G%C3%B6del
%27s_incompleteness_theorems • http://en.wikipedia.org/wiki/Chaos_theory • http://en.wikipedia.org/wiki/Complex_systems • http://en.wikipedia.org/wiki/Systems_theory• http://en.wikipedia.org/wiki/Prigogine• http://en.wikipedia.org/wiki/Dispersed_knowledge
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Ako vôbec môže niekto taký ako my - výsledok slepých zápasov evolúcie - dosiahnuť akúkoľvek formu neomylnosti?Preto musím vo vede stále pripustiť,
že sa môžem vo všetkom mýliť.
To však znamená aj to, že sa môžem mýliť v tom, že sa vo všetkom mýlim.
Intellectuals … are not original thinkers, but purveyors of second-hand ideas (F. Hayek)
