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

Review of Austrian Economics

Volumn 20, Issue 2-3, 2007, Pages 105-122

  Axtell, Robert L ^a,b  

^a GEORGE MASON UNIVERSITY   (United States)

^b SANTA FE INSTITUTE   (United States)

Author keywords

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

Indexed keywords

EID: 34249895291     PISSN: 08893047     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11138-007-0021-5     Document Type: Article

References (91)

1. Albin, P. S. (1975). The analysis of complex socioeconomic systems. Lexington, MA: Lexington Books, DC Heath & Company.
2. Albin, P. S. (1998). Barriers and bounds to rationality: Essays on economic dynamics in interactive systems. Princeton, NJ: Princeton University Press.
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.
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.
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.
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.
7. Axtell, R. L. (2005). The complexity of exchange. Economic Journal 7/5(504), F193210.
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.
9. Baas, N. A. (1994). Emergence, hierarchies, and hyperstructures. In C. G. Langton (Ed.), Artificial life III. Reading, MA: Addison-Wesley Publishing.
10. Blume, L. (1993). The statistical mechanics of strategic interaction. Games and Economic Behavior, 5, 387-424.
11. Blume, L. (1995). The statistical mechanics of best-response strategy revision. Games and Economic Behavior, 11, 111-145.
12. Bousquet, F. (1996). Fishermen's society. In N. Gilbert & J. Doran (Eds.), Simulating societies. London: UCL Press.
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.
14. Buchanan, J. M. (1964). What should economists do? Southern Economic Journal, 30(3), 213-222.
15. Camerer, C. (1997). Progress in behavioral game theory. Journal of Economic Perspectives, 11(4), 167-188.
16. Camerer, C. (2003). Behavioral game theory. Princeton, NJ: Princeton University Press.
17. Cartwright, N. (1983). How the laws of physics lie. New York, NY: Clarendon Press, Oxford University Press.
18. Codd, E. F. (1968). Cellular automata. New York, NY: Academic Press.
19. Conitzer, V., & Sandholm, T. (2002). Complexity of Mechanism Design. Proceedings of the Uncertainty in Artifical Intelligence Conference. Edmonton, Canada.
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.
21. Cont, R., Ghoulmie, F., & Nadal, J.-P. (2005). Heterogeneity and feedback in an agent-based market model. Journal of Physics. Condensed Matter, 77(14), S1259-S1268.
22. Darley, V. (1994). Emergent phenomena and complexity. In R. A. Brooks & P. Maes (Eds.), Artificial Live IV. Cambridge, MA: MIT Press.
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.
24. Davies, M., & Stone, T. (Eds.) (1995). Mental simulation. Blackwell Publishers.
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.
26. Ermentrout, G. B., & Edelstein-Keshet, L. (1993). Cellular automata approaches to biological modeling. Journal of Theoretical Biology, 160, 97-113.
27. Faith, J. (1998). Why gliders don't exist: Anti-reductionism and emergence. In C. Adami, R. K. Belew, H. Kitauo, & C. E. Taylor (Eds.), Artificial Life VI (pp. 389-392). Cambridge, MA: MIT Press.
28. Foley, D. K. (1994). A statistical equilibrium theory of markets. Journal of Economic Theory, 62, 321-345.
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.
30. Gilbert, N., & Conte, R. (Eds.) (1995). Artificial societies: The computer simulation of social life. London: UCL Press.
31. Gilbert, N., & Doran, J. (Eds.) (1994). Simulating societies: The computer simulation of social phenomena. London: UCL Press.
32. Gilbert, N., & Troitzsch, K. G. (1999). Simulation for the social scientist. Buckingham, United Kingdom: Open University Press.
33. Gintis, H. (2004). Towards the unity of the human behavioral sciences. Politics, Philosophy & Economics, 3 (1), 37-57.
34. Glimcher, P. W (2003). Decisions, uncertainty and the brain: The science of neuroeconomics. Cambridge, MA: MIT Press.
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.
36. Grimm, V., & Railsback, S. F. (2005). Individual-based modeling and ecology. Princeton, NJ: Princeton University Press.
37. Gutowitz, H. (1990). Cellular automata: From theory to practice. Cambridge, MA: MIT Press.
38. Gutowitz, H. (Ed.) (1991). Cellular automata: Theory and experiment. Cambridge, MA: MIT Press.
39. Halm, F. H. (1962). On the stability of pure exchange equilibrium. International Economic Review, 3(2), 206-213.
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.
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.
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.
43. Hales, D. (2002). Evolving specialisation, altruism and group-level optimisation using tags. In J. S. Sichman, F. Bousquet, & P. Davidssou (Eds.), Multi-agent-based simulation II, vol. 2581 (pp. 26-35). Berlin: Springer-Verlag.
44. Hayek, F. A. V. (1945). The use of knowledge in society. American Economic Review, 35(4), 519-530.
45. Holland, J. H. (1995). Hidden order: How adaptation builds complexity. New York, NY: Perseus Press.
46. Holland, J. H. (1998). Emergence: From chaos to order. Reading, MA: Perseus.
47. Howitt, P., & Clower, R. (2000). The emergence of economic organization. Journal of Economic Behavior and Organization, 41(1), 55-84.
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.
49. Johnson, S. (2001). Emergence: The connected lives of ants, brains, cities and software. New York, NY: Scribner.
50. Kirman, A. P. (1992). Whom or what does the representative agent represent? Journal of Economic Perspectives, 6(2), 117-136.
51. Kirman, A. P. (1993). Ants, rationality and recruitment. Quarterly Journal of Economics, 108, 137-156.
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.
53. Kohn, M. (2004). Value and exchange. The Cato Journal, 24(3), 303-339.
54. Langton, C. G. (1995). Artificial life: An overview. Cambridge, MA: MIT Press.
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.
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.
57. LeBaron, B. (2001b). Evolution and time horizons in an agent-based stock market. Macroeconomic Dynamics, 5, 225-254.
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 Slates of America, 99(suppl 3), 7201-7206.
59. Liggett, T. (1985). Interacting Particle Systems. New York, N.Y., Springer-Verlag.
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.
61. Mirowski, P. (1989). More heat than light: Economics and social physics, physics as nature's economics. New York, NY: Cambridge University Press.
62. Mirowski, P. (2001). Machine dreams: How economics became a Cyborg science. New York, NY: Cambridge University Press.
63. Morowitz, H. J. (1998). Emergence and equilibrium. Complexity, 4(6), 12-13.
64. Morowitz, H. J. (2002). The emergence of everything: How the world became complex. New York, NY: Oxford University Press.
65. Negishi, T. (1961). On the formation of prices. International Economic Review, 2(1), 122-126.
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.
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.
68. Papadimitriou, C. H. (1994). On the complexity of Ihe parity argument and other inefficient proofs of existence. Journal of Computer and Systems Sciences, 48, 498-532.
69. Papadimitriou, C., & Yanuakakis, 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.
70. Riolo, R. L., Axelrod, R., & Cohen, M. D. (2001). Evolution of cooperation without reciprocity. Nature, 414, 441-443.
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.
72. Sawyer, R. K. (2002). Emergence in sociology: Contemporary philosophy of mind and some implications for sociological theory. American Journal of Sociology, 108.
73. Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143-186.
74. Schelling, T. C. (1978). Micromotives and macrobehavior. New York, NY: Norton.
75. Simon, H. A. (1957). Models of man: Social and rational. New York, NY: John Wiley & Sons, Inc.
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.
77. Simon, H. A. (1978). On how to decide what to do. Bell Journal of Economics, 9(2), 494-507.
78. Simon, H. A. (1997a). Models of bounded rationality: Behavioral economics and business organizations. Cambridge, MA: MIT Press.
79. Simon, H. A. (1997b). Models of bounded rationality: Economic analysis and public policy. Cambridge, MA: MIT Press.
80. Simon, H. A. (1997c). Models of bounded rationality: Empirically grounded economic reason. Cambridge, MA: MIT Press.
81. Smith, A. (1976 [1776]). An inquiry into the nature and causes of the wealth of nations. New York, NY: Oxford University Press.
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.
83. Tesfatsion, L. (2002). Agent-based computational economics: Growing economies from the bottom up. Artificial Life, 8(1), 55-82.
84. Tesfatsion, L. (2003). Agent-based computational economics: Modeling economies as complex adaptive systems. Information Sciences, 149(4), 262-268.
85. Toffoli, T., & Margolus, N. (1987). Cellular automata machines: A new environment for modeling. Cambridge, MA: MIT Press.
86. Uzawa, H. (1962). On the stability of Edgeworth's barter process. International Economic Review, 3(2), 218-232.
87. von Neumann, J., & Morgenstern, O. (1944 [1980]). Games and economic behavior. Princeton, NJ: Princeton University Press.
88. Wegner, P. (1997). Why interaction is more powerful than algorithms. Communications of the ACM, 40(5), 80-91.
89. Wegner, P., & Goldin, D. (2003). Computation beyond taring machines. Communications of the ACM, 46(4), 100-102.
90. Wolfram, S. (1994). Cellular automata and complexity. Reading, MA: Addison-Wesley.
91. Wolfram, S. (2002). A new kind of science. Champaign, IL: Wolfram Media.