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

- 626 Downloads
- 26 Citations

## 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## Notes

### Acknowledgement

The author thank the organizers of the symposium for their helpful comments.

## References

- Albin, P. S. (1975).
*The analysis of complex socioeconomic systems*. Lexington, MA: Lexington Books, DC Heath & Company.Google Scholar - Albin, P. S. (1998).
*Barriers and bounds to rationality: Essays on economic dynamics in interactive systems*. Princeton, NJ: Princeton University Press.Google Scholar - 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 - 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 - 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 - 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 - Axtell, R. L. (2005). The complexity of exchange.
*Economic Journal, 115*(504), F193210.CrossRefGoogle Scholar - 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 - Baas, N. A. (1994). Emergence, hierarchies, and hyperstructures. In C. G. Langton (Ed.),
*Artificial life III*. Reading, MA: Addison-Wesley Publishing.Google Scholar - Blume, L. (1993). The statistical mechanics of strategic interaction.
*Games and Economic Behavior, 5*, 387–424.CrossRefGoogle Scholar - Blume, L. (1995). The statistical mechanics of best-response strategy revision.
*Games and Economic Behavior, 11*, 111–145.CrossRefGoogle Scholar - Bousquet, F. (1996). Fishermen’s society. In N. Gilbert & J. Doran (Eds.),
*Simulating societies*. London: UCL Press.Google Scholar - 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 - Buchanan, J. M. (1964). What should economists do?
*Southern Economic Journal, 30*(3), 213–222.CrossRefGoogle Scholar - Camerer, C. (1997). Progress in behavioral game theory.
*Journal of Economic Perspectives, 11*(4), 167–188.Google Scholar - Camerer, C. (2003).
*Behavioral game theory*. Princeton, NJ: Princeton University Press.Google Scholar - Cartwright, N. (1983).
*How the laws of physics lie*. New York, NY: Clarendon Press, Oxford University Press.Google Scholar - Codd, E. F. (1968).
*Cellular automata*. New York, NY: Academic Press.Google Scholar - Conitzer, V., & Sandholm, T. (2002). Complexity of Mechanism Design.
*Proceedings of the Uncertainty in Artifical Intelligence Conference*. Edmonton, Canada.Google Scholar - 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 - 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 - Darley, V. (1994). Emergent phenomena and complexity. In R. A. Brooks & P. Maes (Eds.),
*Artificial Live IV*. Cambridge, MA: MIT Press.Google Scholar - 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 - Davies, M., & Stone, T. (Eds.) (1995).
*Mental simulation*. Blackwell Publishers.Google Scholar - 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 - Ermentrout, G. B., & Edelstein-Keshet, L. (1993). Cellular automata approaches to biological modeling.
*Journal of Theoretical Biology, 160*, 97–113.CrossRefGoogle Scholar - 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 - Foley, D. K. (1994). A statistical equilibrium theory of markets.
*Journal of Economic Theory, 62*, 321–345.CrossRefGoogle Scholar - 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 - Gilbert, N., & Conte, R. (Eds.) (1995).
*Artificial societies: The computer simulation of social life*. London: UCL Press.Google Scholar - Gilbert, N., & Doran, J. (Eds.) (1994).
*Simulating societies: The computer simulation of social phenomena*. London: UCL Press.Google Scholar - Gilbert, N., & Troitzsch, K. G. (1999).
*Simulation for the social scientist*. Buckingham, United Kingdom: Open University Press.Google Scholar - Gintis, H. (2004). Towards the unity of the human behavioral sciences.
*Politics, Philosophy & Economics, 3*(1), 37–57.CrossRefGoogle Scholar - Glimcher, P. W. (2003).
*Decisions, uncertainty and the brain: The science of neuroeconomics*. Cambridge, MA: MIT Press.Google Scholar - 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 - Grimm, V., & Railsback, S. F. (2005).
*Individual-based modeling and ecology*. Princeton, NJ: Princeton University Press.Google Scholar - Gutowitz, H. (1990).
*Cellular automata: From theory to practice*. Cambridge, MA: MIT Press.Google Scholar - Gutowitz, H. (Ed.) (1991).
*Cellular automata: Theory and experiment*. Cambridge, MA: MIT Press.Google Scholar - Hahn, F. H. (1962). On the stability of pure exchange equilibrium.
*International Economic Review, 3*(2), 206–213.CrossRefGoogle Scholar - 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 - 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 - 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 - 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 - Hayek, F. A. V. (1945). The use of knowledge in society.
*American Economic Review, 35*(4), 519–530.Google Scholar - Holland, J. H. (1995).
*Hidden order: How adaptation builds complexity*. New York, NY: Perseus Press.Google Scholar - Holland, J. H. (1998).
*Emergence: From chaos to order*. Reading, MA: Perseus.Google Scholar - Howitt, P., & Clower, R. (2000). The emergence of economic organization.
*Journal of Economic Behavior and Organization, 41*(1), 55–84.CrossRefGoogle Scholar - 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 - Johnson, S. (2001).
*Emergence: The connected lives of ants, brains, cities and software*. New York, NY: Scribner.Google Scholar - Kirman, A. P. (1992). Whom or what does the representative agent represent?
*Journal of Economic Perspectives, 6*(2), 117–136.Google Scholar - Kirman, A. P. (1993). Ants, rationality and recruitment.
*Quarterly Journal of Economics, 108*, 137–156.CrossRefGoogle Scholar - 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 - Kohn, M. (2004). Value and exchange.
*The Cato Journal, 24*(3), 303–339.Google Scholar - Langton, C. G. (1995).
*Artificial life: An overview*. Cambridge, MA: MIT Press.Google Scholar - 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 - 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 - LeBaron, B. (2001b). Evolution and time horizons in an agent-based stock market.
*Macroeconomic Dynamics, 5*, 225–254.CrossRefGoogle Scholar - 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 - Liggett, T. (1985).
*Interacting Particle Systems*. New York, N.Y., Springer-Verlag.Google Scholar - 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 - Mirowski, P. (1989).
*More heat than light: Economics and social physics, physics as nature’s economics*. New York, NY: Cambridge University Press.Google Scholar - Mirowski, P. (2001).
*Machine dreams: How economics became a Cyborg science*. New York, NY: Cambridge University Press.Google Scholar - Morowitz, H. J. (1998). Emergence and equilibrium.
*Complexity, 4*(6), 12–13.CrossRefGoogle Scholar - Morowitz, H. J. (2002).
*The emergence of everything: How the world became complex*. New York, NY: Oxford University Press.Google Scholar - Negishi, T. (1961). On the formation of prices.
*International Economic Review, 2*(1), 122–126.CrossRefGoogle Scholar - 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 - 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 - 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 - 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 - Riolo, R. L., Axelrod, R., & Cohen, M. D. (2001). Evolution of cooperation without reciprocity.
*Nature, 414*, 441–443.CrossRefGoogle Scholar - 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 - Sawyer, R. K. (2002). Emergence in sociology: Contemporary philosophy of mind and some implications for sociological theory.
*American Journal of Sociology, 108*.Google Scholar - Schelling, T. C. (1971). Dynamic models of segregation.
*Journal of Mathematical Sociology, 1*, 143–186.CrossRefGoogle Scholar - Schelling, T. C. (1978).
*Micromotives and macrobehavior*. New York, NY: Norton.Google Scholar - Simon, H. A. (1957).
*Models of man: Social and rational*. New York, NY: John Wiley & Sons, Inc.Google Scholar - 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 - Simon, H. A. (1978). On how to decide what to do.
*Bell Journal of Economics, 9*(2), 494–507.CrossRefGoogle Scholar - Simon, H. A. (1997a).
*Models of bounded rationality: Behavioral economics and business organizations*. Cambridge, MA: MIT Press.Google Scholar - Simon, H. A. (1997b).
*Models of bounded rationality: Economic analysis and public policy*. Cambridge, MA: MIT Press.Google Scholar - Simon, H. A. (1997c).
*Models of bounded rationality: Empirically grounded economic reason*. Cambridge, MA: MIT Press.Google Scholar - Smith, A. (1976 [1776]).
*An inquiry into the nature and causes of the wealth of nations*. New York, NY: Oxford University Press.Google Scholar - 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 - Tesfatsion, L. (2002). Agent-based computational economics: Growing economies from the bottom up.
*Artificial Life, 8*(1), 55–82.CrossRefGoogle Scholar - Tesfatsion, L. (2003). Agent-based computational economics: Modeling economies as complex adaptive systems.
*Information Sciences, 149*(4), 262–268.CrossRefGoogle Scholar - Toffoli, T., & Margolus, N. (1987).
*Cellular automata machines: A new environment for modeling*. Cambridge, MA: MIT Press.Google Scholar - Uzawa, H. (1962). On the stability of Edgeworth’s barter process.
*International Economic Review, 3*(2), 218–232.CrossRefGoogle Scholar - von Neumann, J., & Morgenstern, O. (1944 [1980]).
*Games and economic behavior*. Princeton, NJ: Princeton University Press.Google Scholar - Wegner, P. (1997). Why interaction is more powerful than algorithms.
*Communications of the ACM, 40*(5), 80–91.CrossRefGoogle Scholar - Wegner, P., & Goldin, D. (2003). Computation beyond turing machines.
*Communications of the ACM, 46*(4), 100–102.CrossRefGoogle Scholar - Wolfram, S. (1994).
*Cellular automata and complexity*. Reading, MA: Addison-Wesley.Google Scholar - Wolfram, S. (2002).
*A new kind of science*. Champaign, IL: Wolfram Media.Google Scholar