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.
Similar content being viewed by others
Notes
“[E]conomics as a discipline has no special claim to understanding the nature of individual behavior; presumably psychologists and cognitive scientists have much more to say about it. The stock in trade of economics, rather, is its understanding of the aggregate outcome of individual behavior—or more precisely, of the ‘unintended consequences’ of intended actions.”
Buchanan (1964) makes essentially the same point in discussing Robinson Crusoe’s solitary allocation problem that becomes ‘symbiotic’ once Friday arrives and the two are brought into association with one another.
“For the exchange paradigm, the concept of market failure is meaningless” (Kohn 2004: 325).
References
Albin, P. S. (1975). The analysis of complex socioeconomic systems. Lexington, MA: Lexington Books, DC Heath & Company.
Albin, P. S. (1998). Barriers and bounds to rationality: Essays on economic dynamics in interactive systems. Princeton, NJ: Princeton University Press.
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.
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.
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.
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.
Axtell, R. L. (2005). The complexity of exchange. Economic Journal, 115(504), F193210.
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.
Baas, N. A. (1994). Emergence, hierarchies, and hyperstructures. In C. G. Langton (Ed.), Artificial life III. Reading, MA: Addison-Wesley Publishing.
Blume, L. (1993). The statistical mechanics of strategic interaction. Games and Economic Behavior, 5, 387–424.
Blume, L. (1995). The statistical mechanics of best-response strategy revision. Games and Economic Behavior, 11, 111–145.
Bousquet, F. (1996). Fishermen’s society. In N. Gilbert & J. Doran (Eds.), Simulating societies. London: UCL Press.
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.
Buchanan, J. M. (1964). What should economists do? Southern Economic Journal, 30(3), 213–222.
Camerer, C. (1997). Progress in behavioral game theory. Journal of Economic Perspectives, 11(4), 167–188.
Camerer, C. (2003). Behavioral game theory. Princeton, NJ: Princeton University Press.
Cartwright, N. (1983). How the laws of physics lie. New York, NY: Clarendon Press, Oxford University Press.
Codd, E. F. (1968). Cellular automata. New York, NY: Academic Press.
Conitzer, V., & Sandholm, T. (2002). Complexity of Mechanism Design. Proceedings of the Uncertainty in Artifical Intelligence Conference. Edmonton, Canada.
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.
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.
Darley, V. (1994). Emergent phenomena and complexity. In R. A. Brooks & P. Maes (Eds.), Artificial Live IV. Cambridge, MA: MIT Press.
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.
Davies, M., & Stone, T. (Eds.) (1995). Mental simulation. Blackwell Publishers.
Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Washington, DC/Cambridge, MA: Brookings Institution Press/MIT Press.
Ermentrout, G. B., & Edelstein-Keshet, L. (1993). Cellular automata approaches to biological modeling. Journal of Theoretical Biology, 160, 97–113.
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.
Foley, D. K. (1994). A statistical equilibrium theory of markets. Journal of Economic Theory, 62, 321–345.
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.
Gilbert, N., & Conte, R. (Eds.) (1995). Artificial societies: The computer simulation of social life. London: UCL Press.
Gilbert, N., & Doran, J. (Eds.) (1994). Simulating societies: The computer simulation of social phenomena. London: UCL Press.
Gilbert, N., & Troitzsch, K. G. (1999). Simulation for the social scientist. Buckingham, United Kingdom: Open University Press.
Gintis, H. (2004). Towards the unity of the human behavioral sciences. Politics, Philosophy & Economics, 3(1), 37–57.
Glimcher, P. W. (2003). Decisions, uncertainty and the brain: The science of neuroeconomics. Cambridge, MA: MIT Press.
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.
Grimm, V., & Railsback, S. F. (2005). Individual-based modeling and ecology. Princeton, NJ: Princeton University Press.
Gutowitz, H. (1990). Cellular automata: From theory to practice. Cambridge, MA: MIT Press.
Gutowitz, H. (Ed.) (1991). Cellular automata: Theory and experiment. Cambridge, MA: MIT Press.
Hahn, F. H. (1962). On the stability of pure exchange equilibrium. International Economic Review, 3(2), 206–213.
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.
Haken, H. (1987). Synergetics: An approach to self organization. In F. E. Yates (Ed.), Self-organizing systems: The emergence of order. Berlin: Plenum Press.
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.
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.
Hayek, F. A. V. (1945). The use of knowledge in society. American Economic Review, 35(4), 519–530.
Holland, J. H. (1995). Hidden order: How adaptation builds complexity. New York, NY: Perseus Press.
Holland, J. H. (1998). Emergence: From chaos to order. Reading, MA: Perseus.
Howitt, P., & Clower, R. (2000). The emergence of economic organization. Journal of Economic Behavior and Organization, 41(1), 55–84.
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.
Johnson, S. (2001). Emergence: The connected lives of ants, brains, cities and software. New York, NY: Scribner.
Kirman, A. P. (1992). Whom or what does the representative agent represent? Journal of Economic Perspectives, 6(2), 117–136.
Kirman, A. P. (1993). Ants, rationality and recruitment. Quarterly Journal of Economics, 108, 137–156.
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.
Kohn, M. (2004). Value and exchange. The Cato Journal, 24(3), 303–339.
Langton, C. G. (1995). Artificial life: An overview. Cambridge, MA: MIT Press.
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.
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.
LeBaron, B. (2001b). Evolution and time horizons in an agent-based stock market. Macroeconomic Dynamics, 5, 225–254.
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.
Liggett, T. (1985). Interacting Particle Systems. New York, N.Y., Springer-Verlag.
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.
Mirowski, P. (1989). More heat than light: Economics and social physics, physics as nature’s economics. New York, NY: Cambridge University Press.
Mirowski, P. (2001). Machine dreams: How economics became a Cyborg science. New York, NY: Cambridge University Press.
Morowitz, H. J. (1998). Emergence and equilibrium. Complexity, 4(6), 12–13.
Morowitz, H. J. (2002). The emergence of everything: How the world became complex. New York, NY: Oxford University Press.
Negishi, T. (1961). On the formation of prices. International Economic Review, 2(1), 122–126.
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.
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.
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.
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.
Riolo, R. L., Axelrod, R., & Cohen, M. D. (2001). Evolution of cooperation without reciprocity. Nature, 414, 441–443.
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.
Sawyer, R. K. (2002). Emergence in sociology: Contemporary philosophy of mind and some implications for sociological theory. American Journal of Sociology, 108.
Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143–186.
Schelling, T. C. (1978). Micromotives and macrobehavior. New York, NY: Norton.
Simon, H. A. (1957). Models of man: Social and rational. New York, NY: John Wiley & Sons, Inc.
Simon, H. A. (1976). From substantive to procedural rationality. In S. Latsis (Ed.), Method and appraisal in economics. New York, NY: Cambridge University Press.
Simon, H. A. (1978). On how to decide what to do. Bell Journal of Economics, 9(2), 494–507.
Simon, H. A. (1997a). Models of bounded rationality: Behavioral economics and business organizations. Cambridge, MA: MIT Press.
Simon, H. A. (1997b). Models of bounded rationality: Economic analysis and public policy. Cambridge, MA: MIT Press.
Simon, H. A. (1997c). Models of bounded rationality: Empirically grounded economic reason. Cambridge, MA: MIT Press.
Smith, A. (1976 [1776]). An inquiry into the nature and causes of the wealth of nations. New York, NY: Oxford University Press.
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.
Tesfatsion, L. (2002). Agent-based computational economics: Growing economies from the bottom up. Artificial Life, 8(1), 55–82.
Tesfatsion, L. (2003). Agent-based computational economics: Modeling economies as complex adaptive systems. Information Sciences, 149(4), 262–268.
Toffoli, T., & Margolus, N. (1987). Cellular automata machines: A new environment for modeling. Cambridge, MA: MIT Press.
Uzawa, H. (1962). On the stability of Edgeworth’s barter process. International Economic Review, 3(2), 218–232.
von Neumann, J., & Morgenstern, O. (1944 [1980]). Games and economic behavior. Princeton, NJ: Princeton University Press.
Wegner, P. (1997). Why interaction is more powerful than algorithms. Communications of the ACM, 40(5), 80–91.
Wegner, P., & Goldin, D. (2003). Computation beyond turing machines. Communications of the ACM, 46(4), 100–102.
Wolfram, S. (1994). Cellular automata and complexity. Reading, MA: Addison-Wesley.
Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media.
Acknowledgement
The author thank the organizers of the symposium for their helpful comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Axtell, R.L. What economic agents do: How cognition and interaction lead to emergence and complexity. Rev Austrian Econ 20, 105–122 (2007). https://doi.org/10.1007/s11138-007-0021-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11138-007-0021-5