The Review of Austrian Economics

, Volume 20, Issue 2–3, pp 105–122 | Cite as

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

Article

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.

Keywords

Agent-based modeling Heterogeneous agents Self-organizing systems Emergence Complexity 

JEL Codes

B4 D5 D8 

References

  1. Albin, P. S. (1975). The analysis of complex socioeconomic systems. Lexington, MA: Lexington Books, DC Heath & Company.Google Scholar
  2. Albin, P. S. (1998). Barriers and bounds to rationality: Essays on economic dynamics in interactive systems. Princeton, NJ: Princeton University Press.Google Scholar
  3. Arthur, W. B., Holland, J. H., LeBaron, B., Palmer, R., & Tayler, P. (1997). Asset pricing under endogenous expectations in an artificial stock market. In W. B. Arthur, S. N. Durlauf, & D. A. Lane (Eds.), The economy as an evolving complex system II. Reading, MA: Addison-Wesley.Google Scholar
  4. Ashlock, D., Smucker, M. D., Stanley, E. A., & Tesfatsion L. (1996). Preferential partner selection in an evolutionary study of prisoner’s dilemma. Biosystems, 37, 99–125.CrossRefGoogle Scholar
  5. Axtell, R. L. (2000). Why agents? On the varied motivations for agent computing in the social sciences. In C. M. Macal & D. Sallach (Eds.), Proceedings of the workshop on agent simulation: Applications, models, and tools (pp. 3–24). Chicago, IL: Argonne National Laboratory.Google Scholar
  6. Axtell, R. L. (2002). Non-cooperative dynamics of multi-agent teams. In C. Castelfranchi & W. L. Johnson (Eds.), Proceedings of the first international joint conference on autonomous agents and multiagent systems Part 3 (pp. 1082–1089). Bologna, Italy: ACM Press.CrossRefGoogle Scholar
  7. Axtell, R. L. (2005). The complexity of exchange. Economic Journal, 115(504), F193210.CrossRefGoogle Scholar
  8. Axtell, R. L. (2006). Multi-agent systems macro: A prospectus. In D. C. Colander (Ed.), Post walrasian macroeconomics: Beyond the dynamic stochastic general equilibrium model. New York, NY: Cambridge University Press.Google Scholar
  9. Baas, N. A. (1994). Emergence, hierarchies, and hyperstructures. In C. G. Langton (Ed.), Artificial life III. Reading, MA: Addison-Wesley Publishing.Google Scholar
  10. Blume, L. (1993). The statistical mechanics of strategic interaction. Games and Economic Behavior, 5, 387–424.CrossRefGoogle Scholar
  11. Blume, L. (1995). The statistical mechanics of best-response strategy revision. Games and Economic Behavior, 11, 111–145.CrossRefGoogle Scholar
  12. Bousquet, F. (1996). Fishermen’s society. In N. Gilbert & J. Doran (Eds.), Simulating societies. London: UCL Press.Google Scholar
  13. Bradburd, R., Sheppard, S., Bergeron, J., & Engler E. (2006). The impact of rent controls in Non-Walrasian markets: An agent-based modelling approach. Journal of Regional Science, 46(3), 455–491.CrossRefGoogle Scholar
  14. Buchanan, J. M. (1964). What should economists do? Southern Economic Journal, 30(3), 213–222.CrossRefGoogle Scholar
  15. Camerer, C. (1997). Progress in behavioral game theory. Journal of Economic Perspectives, 11(4), 167–188.Google Scholar
  16. Camerer, C. (2003). Behavioral game theory. Princeton, NJ: Princeton University Press.Google Scholar
  17. Cartwright, N. (1983). How the laws of physics lie. New York, NY: Clarendon Press, Oxford University Press.Google Scholar
  18. Codd, E. F. (1968). Cellular automata. New York, NY: Academic Press.Google Scholar
  19. Conitzer, V., & Sandholm, T. (2002). Complexity of Mechanism Design. Proceedings of the Uncertainty in Artifical Intelligence Conference. Edmonton, Canada.Google Scholar
  20. Cont, R. (2006). Volatility clustering in financial markets: Empirical facts and agent-based models. In A. P. Kirman & G. Teyssiere (Eds.), Long memory in economics. New York, NY: Springer.Google Scholar
  21. Cont, R., Ghoulmie, F., & Nadal, J.-P. (2005). Heterogeneity and feedback in an agent-based market model. Journal of Physics. Condensed Matter, 17(14), S1259–S1268.CrossRefGoogle Scholar
  22. Darley, V. (1994). Emergent phenomena and complexity. In R. A. Brooks & P. Maes (Eds.), Artificial Live IV. Cambridge, MA: MIT Press.Google Scholar
  23. Darley, V., Outkin, A., Plate, T., & Gao, F. (2001). Learning, evolution and tick size effects in a simulation of the NASDAQ stock market. Proceedings of the 5th world multi-conference on systemics, cybernetics and informatics (SCI 2001). Orlando, FL.: International Institute for Informatics and Systematics.Google Scholar
  24. Davies, M., & Stone, T. (Eds.) (1995). Mental simulation. Blackwell Publishers.Google Scholar
  25. Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Washington, DC/Cambridge, MA: Brookings Institution Press/MIT Press.Google Scholar
  26. Ermentrout, G. B., & Edelstein-Keshet, L. (1993). Cellular automata approaches to biological modeling. Journal of Theoretical Biology, 160, 97–113.CrossRefGoogle Scholar
  27. Faith, J. (1998). Why gliders don’t exist: Anti-reductionism and emergence. In C. Adami, R. K. Belew, H. Kitano, & C. E. Taylor (Eds.), Artificial Life VI (pp. 389–392). Cambridge, MA: MIT Press.Google Scholar
  28. Foley, D. K. (1994). A statistical equilibrium theory of markets. Journal of Economic Theory, 62, 321–345.CrossRefGoogle Scholar
  29. Fontana, W., & Buss, L. (1994). What would be conserved if ‘The tape were played twice’? Proceedings of the National Academy of Sciences of the United States of America, 91, 751–761.CrossRefGoogle Scholar
  30. Gilbert, N., & Conte, R. (Eds.) (1995). Artificial societies: The computer simulation of social life. London: UCL Press.Google Scholar
  31. Gilbert, N., & Doran, J. (Eds.) (1994). Simulating societies: The computer simulation of social phenomena. London: UCL Press.Google Scholar
  32. Gilbert, N., & Troitzsch, K. G. (1999). Simulation for the social scientist. Buckingham, United Kingdom: Open University Press.Google Scholar
  33. Gintis, H. (2004). Towards the unity of the human behavioral sciences. Politics, Philosophy & Economics, 3(1), 37–57.CrossRefGoogle Scholar
  34. Glimcher, P. W. (2003). Decisions, uncertainty and the brain: The science of neuroeconomics. Cambridge, MA: MIT Press.Google Scholar
  35. Grimm, V. (1999). Ten years of individual-based modelling in ecology: What have we learned and what could we learn in the future? Ecological Modelling, 115, 129–148.CrossRefGoogle Scholar
  36. Grimm, V., & Railsback, S. F. (2005). Individual-based modeling and ecology. Princeton, NJ: Princeton University Press.Google Scholar
  37. Gutowitz, H. (1990). Cellular automata: From theory to practice. Cambridge, MA: MIT Press.Google Scholar
  38. Gutowitz, H. (Ed.) (1991). Cellular automata: Theory and experiment. Cambridge, MA: MIT Press.Google Scholar
  39. Hahn, F. H. (1962). On the stability of pure exchange equilibrium. International Economic Review, 3(2), 206–213.CrossRefGoogle Scholar
  40. Hahn, R. W. (1989). Economic prescriptions for environmental problems: How the patient followed the doctor’s orders. Journal of Economic Perspectives, 3(2), 95–114.Google Scholar
  41. Haken, H. (1987). Synergetics: An approach to self organization. In F. E. Yates (Ed.), Self-organizing systems: The emergence of order. Berlin: Plenum Press.Google Scholar
  42. Hales, D. (2001). Cooperation without memory or space: Tags, groups and the prisoner’s dilemma. In S. Moss & P. Davidsson (Eds.), Multi-agent-based simulation, vol. 1979 (pp. 157–166). Heidelberg, Germany: Springer-Verlag.Google Scholar
  43. Hales, D. (2002). Evolving specialisation, altruism and group-level optimisation using tags. In J. S. Sichman, F. Bousquet, & P. Davidsson (Eds.), Multi-agent-based simulation II, vol. 2581 (pp. 26–35). Berlin: Springer-Verlag.Google Scholar
  44. Hayek, F. A. V. (1945). The use of knowledge in society. American Economic Review, 35(4), 519–530.Google Scholar
  45. Holland, J. H. (1995). Hidden order: How adaptation builds complexity. New York, NY: Perseus Press.Google Scholar
  46. Holland, J. H. (1998). Emergence: From chaos to order. Reading, MA: Perseus.Google Scholar
  47. Howitt, P., & Clower, R. (2000). The emergence of economic organization. Journal of Economic Behavior and Organization, 41(1), 55–84.CrossRefGoogle Scholar
  48. Huberman, B. A., & Glance, N. S. (1993). Evolutionary games and computer simulations. Proceedings of the National Academy of Sciences of the United States of America, 90, 7716–7718.CrossRefGoogle Scholar
  49. Johnson, S. (2001). Emergence: The connected lives of ants, brains, cities and software. New York, NY: Scribner.Google Scholar
  50. Kirman, A. P. (1992). Whom or what does the representative agent represent? Journal of Economic Perspectives, 6(2), 117–136.Google Scholar
  51. Kirman, A. P. (1993). Ants, rationality and recruitment. Quarterly Journal of Economics, 108, 137–156.CrossRefGoogle Scholar
  52. Kirman, A. P. (1997). The economy as an interactive system. In W. B. Arthur, S. N. Durlauf, & D. A. Lane (Eds.), The economy as an evolving complex system II. Reading, MA: Addison-Wesley.Google Scholar
  53. Kohn, M. (2004). Value and exchange. The Cato Journal, 24(3), 303–339.Google Scholar
  54. Langton, C. G. (1995). Artificial life: An overview. Cambridge, MA: MIT Press.Google Scholar
  55. Laughlin, R. B., & Pines, D. (2000). The theory of everything. Proceedings of the National Academy of Sciences of the United States of America, 97(1), 28–31.CrossRefGoogle Scholar
  56. LeBaron, B. (2001a). Empirical regularities from interacting long and short memory investors in an agent-based stock market. IEEE Transactions on Evolutionary Computation, 5, 442–455.CrossRefGoogle Scholar
  57. LeBaron, B. (2001b). Evolution and time horizons in an agent-based stock market. Macroeconomic Dynamics, 5, 225–254.CrossRefGoogle Scholar
  58. LeBaron, B. (2002). Short-memory traders and their impact on group learning in financial markets. Proceedings of the National Academy of Sciences of the United States of America, 99(suppl 3), 7201–7206.CrossRefGoogle Scholar
  59. Liggett, T. (1985). Interacting Particle Systems. New York, N.Y., Springer-Verlag.Google Scholar
  60. Lux, T. (1998). The socioeconomic dynamics of speculative markets: Interacting agents, chaos and the fat tails of return distributions. Journal of Economic Behavior and Organization, 33, 143–165.CrossRefGoogle Scholar
  61. Mirowski, P. (1989). More heat than light: Economics and social physics, physics as nature’s economics. New York, NY: Cambridge University Press.Google Scholar
  62. Mirowski, P. (2001). Machine dreams: How economics became a Cyborg science. New York, NY: Cambridge University Press.Google Scholar
  63. Morowitz, H. J. (1998). Emergence and equilibrium. Complexity, 4(6), 12–13.CrossRefGoogle Scholar
  64. Morowitz, H. J. (2002). The emergence of everything: How the world became complex. New York, NY: Oxford University Press.Google Scholar
  65. Negishi, T. (1961). On the formation of prices. International Economic Review, 2(1), 122–126.CrossRefGoogle Scholar
  66. Padgett, J. (1997). The emergence of simple ecologies of skill: A hypercycle approach to economic organization. In W. B. Arthur, S. N. Durlauf, & D. A. Lane (Eds.), The economy as an evolving complex system II. Westview Press.Google Scholar
  67. Palmer, R. G., Arthur, W. B., Holland, J. H., LeBaron, B., & Tayler, P. (1994). Artificial economic life: A simple model of a stock market. Physica Didacta, 75, 264–274.CrossRefGoogle Scholar
  68. Papadimitriou, C. H. (1994). On the complexity of the parity argument and other inefficient proofs of existence. Journal of Computer and Systems Sciences, 48, 498–532.CrossRefGoogle Scholar
  69. Papadimitriou, C., & Yannakakis, M. (1994). On complexity as bounded rationality. Annual ACM Symposium on the Theory of Computing: Proceedings of the Twenty-Sixth Annual ACM Symposium on the Theory of Computing (pp. 726–733). New York, NY: ACM Press.Google Scholar
  70. Riolo, R. L., Axelrod, R., & Cohen, M. D. (2001). Evolution of cooperation without reciprocity. Nature, 414, 441–443.CrossRefGoogle Scholar
  71. Sawyer, R. K. (2001). Simulating emergence and downward causation in small groups. In S. Moss & P. Davidsson (Eds.), Multi-agent-based simulation, vol. 1979 (pp. 49–67). Heidelberg, Germany: Springer-Verlag.Google Scholar
  72. Sawyer, R. K. (2002). Emergence in sociology: Contemporary philosophy of mind and some implications for sociological theory. American Journal of Sociology, 108.Google Scholar
  73. Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143–186.CrossRefGoogle Scholar
  74. Schelling, T. C. (1978). Micromotives and macrobehavior. New York, NY: Norton.Google Scholar
  75. Simon, H. A. (1957). Models of man: Social and rational. New York, NY: John Wiley & Sons, Inc.Google Scholar
  76. Simon, H. A. (1976). From substantive to procedural rationality. In S. Latsis (Ed.), Method and appraisal in economics. New York, NY: Cambridge University Press.Google Scholar
  77. Simon, H. A. (1978). On how to decide what to do. Bell Journal of Economics, 9(2), 494–507.CrossRefGoogle Scholar
  78. Simon, H. A. (1997a). Models of bounded rationality: Behavioral economics and business organizations. Cambridge, MA: MIT Press.Google Scholar
  79. Simon, H. A. (1997b). Models of bounded rationality: Economic analysis and public policy. Cambridge, MA: MIT Press.Google Scholar
  80. Simon, H. A. (1997c). Models of bounded rationality: Empirically grounded economic reason. Cambridge, MA: MIT Press.Google Scholar
  81. Smith, A. (1976 [1776]). An inquiry into the nature and causes of the wealth of nations. New York, NY: Oxford University Press.Google Scholar
  82. Tesfatsion, L. (1997). How economists can get aLife. In W. B. Arthur, S. Durlauf, & D. A. Lane (Eds.), The economy as an evolving complex system, Vol. II. Menlo Park, CA: Addison-Wesley.Google Scholar
  83. Tesfatsion, L. (2002). Agent-based computational economics: Growing economies from the bottom up. Artificial Life, 8(1), 55–82.CrossRefGoogle Scholar
  84. Tesfatsion, L. (2003). Agent-based computational economics: Modeling economies as complex adaptive systems. Information Sciences, 149(4), 262–268.CrossRefGoogle Scholar
  85. Toffoli, T., & Margolus, N. (1987). Cellular automata machines: A new environment for modeling. Cambridge, MA: MIT Press.Google Scholar
  86. Uzawa, H. (1962). On the stability of Edgeworth’s barter process. International Economic Review, 3(2), 218–232.CrossRefGoogle Scholar
  87. von Neumann, J., & Morgenstern, O. (1944 [1980]). Games and economic behavior. Princeton, NJ: Princeton University Press.Google Scholar
  88. Wegner, P. (1997). Why interaction is more powerful than algorithms. Communications of the ACM, 40(5), 80–91.CrossRefGoogle Scholar
  89. Wegner, P., & Goldin, D. (2003). Computation beyond turing machines. Communications of the ACM, 46(4), 100–102.CrossRefGoogle Scholar
  90. Wolfram, S. (1994). Cellular automata and complexity. Reading, MA: Addison-Wesley.Google Scholar
  91. Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Center for Social ComplexityGeorge Mason UniversityFairfaxUSA
  2. 2.Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA
  3. 3.Santa Fe InstituteSanta FeUSA

Personalised recommendations