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What economic agents do: How cognition and interaction lead to emergence and complexity

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  • Robert Axtell

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Abstract

Kohn (The Cato Journal, 24(3):303–339, 2004) has argued that the neoclassical conception of economics—what he terms the “value paradigm”—has experienced diminishing marginal returns for some time. He suggests a new perspective is emerging—one that gives more import to economic processes and less to end states, one that bases behavior less on axioms and more on laboratory experiments. He calls this the “exchange paradigm”. He further asserts that it is the mathematization of economics that is partially at fault for leading the profession down a methodological path that has become something of a dead end. Here I suggest that the nascent research program Kohn has rightly spotted is better understood as distinct from its precursors because it is intrinsically dynamic, permits agent actions out of equilibrium, and treats such actions as occurring within networks. Analyzing economic processes having these characteristics is mathematically very difficult and I concur with Kohn’s appeal to computational approaches. However, I claim it is so-called multi-agent systems and agent-based models that are the way forward within the “exchange paradigm,” and not the cellular automata (Wolfram, A new kind of science, 2002) that Kohn seems to promote. Agent systems are generalizations of cellular automata and support the natural abstraction of individual economic agents as software agents. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Robert Axtell, 2007. "What economic agents do: How cognition and interaction lead to emergence and complexity," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 20(2), pages 105-122, September.
  • Handle: RePEc:kap:revaec:v:20:y:2007:i:2:p:105-122
    DOI: 10.1007/s11138-007-0021-5
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    References listed on IDEAS

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    1. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    2. Blume Lawrence E., 1995. "The Statistical Mechanics of Best-Response Strategy Revision," Games and Economic Behavior, Elsevier, vol. 11(2), pages 111-145, November.
    3. LeBaron, Blake, 2001. "Evolution And Time Horizons In An Agent-Based Stock Market," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 225-254, April.
    4. Leigh TESFATSION, 1995. "How Economists Can Get Alife," Economic Report 37, Iowa State University Department of Economics.
    5. Meir Kohn, 2004. "Value and Exchange," Cato Journal, Cato Journal, Cato Institute, vol. 24(3), pages 303-339, Fall.
    6. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, Oxford University Press, vol. 108(1), pages 137-156.
    7. repec:cto:journl:v:24:y:2004:i:3:p: is not listed on IDEAS
    8. Robert Axtell, 2005. "The Complexity of Exchange," Economic Journal, Royal Economic Society, vol. 115(504), pages 193-210, June.
    9. Foley Duncan K., 1994. "A Statistical Equilibrium Theory of Markets," Journal of Economic Theory, Elsevier, vol. 62(2), pages 321-345, April.
    10. Vincent Darley & Alexander V Outkin, 2007. "Learning, Evolution and Tick Size Effects," World Scientific Book Chapters,in: A Nasdaq Market Simulation Insights on a Major Market from the Science of Complex Adaptive Systems, chapter 5, pages 81-87 World Scientific Publishing Co. Pte. Ltd..
    11. Blume Lawrence E., 1993. "The Statistical Mechanics of Strategic Interaction," Games and Economic Behavior, Elsevier, vol. 5(3), pages 387-424, July.
    12. Ralph Bradburd & Stephen Sheppard & Joseph Bergeron & Eric Engler, 2006. "The Impact Of Rent Controls In Non-Walrasian Markets: An Agent-Based Modeling Approach," Journal of Regional Science, Wiley Blackwell, vol. 46(3), pages 455-491.
    13. Dan Ashlock & Mark D. Smucker & E. Ann Stanley & Leigh Tesfatsion, 1995. "Preferential Partner Selection in an Evolutionary Study of Prisoner's Dilemma," Game Theory and Information 9501002, University Library of Munich, Germany, revised 20 Jan 1995.
    14. Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers Archive 5075, Iowa State University, Department of Economics.
    15. Howitt, Peter & Clower, Robert, 2000. "The emergence of economic organization," Journal of Economic Behavior & Organization, Elsevier, vol. 41(1), pages 55-84, January.
    16. Herbert A. Simon, 1978. "On How to Decide What to Do," Bell Journal of Economics, The RAND Corporation, vol. 9(2), pages 494-507, Autumn.
    17. Hahn, Robert W, 1989. "Economic Prescriptions for Environmental Problems: How the Patient Followed the Doctor's Orders," Journal of Economic Perspectives, American Economic Association, vol. 3(2), pages 95-114, Spring.
    18. Colin F. Camerer, 1997. "Progress in Behavioral Game Theory," Journal of Economic Perspectives, American Economic Association, vol. 11(4), pages 167-188, Fall.
    19. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, January.
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    Cited by:

    1. Friederike Wall, 2016. "Agent-based modeling in managerial science: an illustrative survey and study," Review of Managerial Science, Springer, vol. 10(1), pages 135-193, January.
    2. Sandye Gloria, 2018. "Menger contre Walras," Post-Print hal-01797323, HAL.
    3. Lengnick, Matthias, 2013. "Agent-based macroeconomics: A baseline model," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 102-120.
    4. Christopher J. Coyne, 2010. "Economics as the Study of Coordination and Exchange," Chapters,in: Handbook on Contemporary Austrian Economics, chapter 2 Edward Elgar Publishing.
    5. repec:eee:dyncon:v:83:y:2017:i:c:p:232-269 is not listed on IDEAS
    6. Lengnick, Matthias & Krug, Sebastian & Wohltmann, Hans-Werner, 2013. "Money creation and financial instability: An agent-based credit network approach," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 7, pages 1-44.
    7. Hopfensitz, Astrid & Wranik, Tanja, 2008. "Psychological and environmental determinants of myopic loss aversion," MPRA Paper 9305, University Library of Munich, Germany.
    8. Santiago J. Gangotena, 2017. "Dynamic coordinating non-equilibrium," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 30(1), pages 51-82, March.
    9. Li, Boyao, 2017. "The impact of the Basel III liquidity coverage ratio on macroeconomic stability: An agent-based approach," Economics Discussion Papers 2017-2, Kiel Institute for the World Economy (IfW).
    10. repec:wsi:acsxxx:v:20:y:2017:i:02n03:n:s0219525917500035 is not listed on IDEAS
    11. Ron Martin & Peter Sunley, 2010. "The Place of Path Dependence in an Evolutionary Perspective on the Economic Landscape," Chapters,in: The Handbook of Evolutionary Economic Geography, chapter 3 Edward Elgar Publishing.
    12. repec:col:000093:016019 is not listed on IDEAS
    13. Friederike Wall, 2016. "Agent-based modeling in managerial science: an illustrative survey and study," Review of Managerial Science, Springer, vol. 10(1), pages 135-193, January.
    14. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    15. Alexander William Salter & Solomon Stein, 2016. "Endogenous currency formation in an online environment: The case of Diablo II," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 29(1), pages 53-66, March.
    16. Vipin P. Veetil & Lawrence H. White, 2017. "Towards a New Austrian Macroeconomics," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 30(1), pages 19-38, March.
    17. Andrei, Amanda L. & Comer, Kevin & Koehler, Matthew, 2014. "An agent-based model of network effects on tax compliance and evasion," Journal of Economic Psychology, Elsevier, vol. 40(C), pages 119-133.
    18. Stephan Leitner & Friederike Wall, 2015. "Simulation-based research in management accounting and control: an illustrative overview," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 26(2), pages 105-129, August.
    19. Chad Seagren, 2011. "Examining social processes with agent-based models," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 24(1), pages 1-17, March.
    20. Stephan Leitner & Doris Behrens, 2015. "On the fault (in)tolerance of coordination mechanisms for distributed investment decisions," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(1), pages 251-278, March.

    More about this item

    Keywords

    Agent-based modeling; Heterogeneous agents; Self-organizing systems; Emergence; Complexity; B4; D5; D8;

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • D5 - Microeconomics - - General Equilibrium and Disequilibrium
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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