Tomáš Sabol Ekon ó mia – pohľady z interdisciplinárnych brehov

1. Tomáš Sabol Ekonómia – pohľady z interdisciplinárnych brehov Tomas.Sabol@tuke.sk

2. 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

3. 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

4. I have always believed that the complexity of the problems facing mankind is growing faster than our ability to solve them. Doug Engelbart 4

5. 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 . 5

6. A separate branch of economic and financial analysis, which applies scientific research on human and social, cognitive and emotional factors to better understand economicdecisions 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 6

7. 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) 7

8. 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) 8

9. 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 9

10. 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 10

11. 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 11

12. Products and services are created and valueisaddedthroughsocialnetworksoperating on large or globalscales New bussines models needed Economies of scalestemfromthesizeofthenetwork - nottheenterprise opensystemispreferable to a closedsystem see also Systems theory … Network Economy 12

13. 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 13

14. 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 14

15. ~ 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 15

16. Complexity 16

17. 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 17

18. 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 18

19. 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 19

20. Complexity in pictures 20

21. Complexity in pictures 21

22. 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 emergenceand self-organizationare very important. Complex Adaptive Systems (CAS) 22

23. 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 23

24. 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 24

25. Limit Cycle 25

26. 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 endogenousevolution 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 26

27. 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 27

28. 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. Nouniversal 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) 28

29. 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) 29

30. Complexity Economics (1/3) 30

31. Complexity Economics (2/3) 31

32. Complexity Economics (3/3) • Complex systems emerge and maintain “on the edge of chaos” - the narrow domain between frozen constancy and chaotic turbulence 32

33. Connections:Evolutionary theory ↔Economics One of the fundamental problems in economics -Bounded rationality: How can agents who arenot infinitely rational and donot 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 doesnot evolve well. Evolutionary theory & Economics 33

34. 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 34

35. Paul Krugman: Economics is about what individualsdo The individuals are self-interested The individuals are intelligent 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 35

36. 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 36

37. 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 37

38. 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 38

39. 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 39

40. 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 40

41. 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 41

42. Attention Economy 42

43. 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. groupthink 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 43

44. 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) 44

45. 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 45

46. 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 46

47. „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) 47

48. 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) 48

49. 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) 49

50. 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) 50