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Teaching Agent-Based Computational Economics to Graduate Students

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Leigh Tesfatsion (Iowa State University)

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Abstract

Agent-based computational economics (ACE) is the computational study of economies modelled as evolving decentralized systems of autonomous interacting agents. A key focus of ACE research is understanding how global regularities arise from the bottom up, through the repeated local interactions of autonomous agents channeled through socio-economic institutions, rather than from top down fictitious coordination mechanisms such as imposed market clearing constraints or an assumption of single representative agents. This paper discusses how ACE materials have been introduced into graduate-level courses in macroeconomic theory over the past several years, using an ACE labor market framework for concrete illustration.

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File URL: http://129.3.20.41/eps/comp/papers/9809/9809001.ps.gz
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Publisher Info
Paper provided by EconWPA in its series Computational Economics with number 9809001.

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Length: 38 pages
Date of creation: 15 Sep 1998
Date of revision: 16 Nov 1998
Handle: RePEc:wpa:wuwpco:9809001

Note: Type of Document - postscript ; prepared on PC using dvips applied to a PC-LaTeX DVI file; to print on postscript printer (or on any printer after opening with ps reader); pages: 38 ; figures: included
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Related research
Keywords: agent-based computational economics; evolutionary economics; labor markets; search and matching; C++ framework; evolutionary game; teaching methods;

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Find related papers by JEL classification:
C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
D8 - Microeconomics - - Information, Knowledge, and Uncertainty
E - Macroeconomics and Monetary Economics
J - Labor and Demographic Economics

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References listed on IDEAS
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  1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May. [Downloadable!] (restricted)
  2. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-71, May. [Downloadable!] (restricted)
  3. James Bullard & John Duffy, 1994. "A model of learning and emulation with artificial adaptive agents," Working Papers 1994-014, Federal Reserve Bank of St. Louis. [Downloadable!]
    Other versions:
  4. Leigh Tesfatsion, 1998. "Ex Ante Capacity Effects in Evolutionary Labor Markets with Adaptive Search," Labor and Demography 9811003, EconWPA. [Downloadable!]
  5. Diamond, Peter A, 1982. "Aggregate Demand Management in Search Equilibrium," Journal of Political Economy, University of Chicago Press, vol. 90(5), pages 881-94, October. [Downloadable!] (restricted)
    Other versions:
  6. Leigh TESFATSION, 1995. "How Economists Can Get Alife," Economic Report 37, Iowa State University Department of Economics. [Downloadable!]
  7. Friedman, Daniel, 1991. "Evolutionary Games in Economics," Econometrica, Econometric Society, vol. 59(3), pages 637-66, May. [Downloadable!] (restricted)
  8. Richard R. Nelson, 1995. "Recent Evolutionary Theorizing about Economic Change," Journal of Economic Literature, American Economic Association, vol. 33(1), pages 48-90, March. [Downloadable!] (restricted)
  9. Armen A. Alchian, 1950. "Uncertainty, Evolution, and Economic Theory," Journal of Political Economy, University of Chicago Press, vol. 58, pages 211. [Downloadable!] (restricted)
  10. Kirman, Alan, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, MIT Press, vol. 108(1), pages 137-56, February. [Downloadable!] (restricted)
  11. Roth, Alvin E. & Sotomayor, Marilda, 1992. "Two-sided matching," Handbook of Game Theory with Economic Applications, in: R.J. Aumann & S. Hart (ed.), Handbook of Game Theory with Economic Applications, edition 1, volume 1, chapter 16, pages 485-541 Elsevier. [Downloadable!] (restricted)
  12. Vriend, Nicolaas J, 1995. "Self-Organization of Markets: An Example of a Computational Approach," Computational Economics, Springer, vol. 8(3), pages 205-31, August.
  13. Arthur, W Brian, 1993. "On Designing Economic Agents That Behave Like Human Agents," Journal of Evolutionary Economics, Springer, vol. 3(1), pages 1-22, February.
  14. Marks, R E, 1992. "Breeding Hybrid Strategies: Optimal Behaviour for Oligopolists," Journal of Evolutionary Economics, Springer, vol. 2(1), pages 17-38, March.
  15. Miller, John H., 1996. "The coevolution of automata in the repeated Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 29(1), pages 87-112, January. [Downloadable!] (restricted)
  16. George Mailath & Larry Samuelson & Avner Shaked, 1994. "Evolution and Endogenous Interactions," Game Theory and Information 9410003, EconWPA. [Downloadable!]
  17. David MCFADZEAN & Leigh TESFATSION, 1996. "A C++ Platform For The Evolution Of Trade Networks," Economic Report 39, Iowa State University Department of Economics. [Downloadable!]
    Other versions:
  18. Kirman, Alan P, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-36, Spring. [Downloadable!] (restricted)
  19. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January. [Downloadable!] (restricted)
  20. 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, EconWPA, revised 20 Jan 1995. [Downloadable!]
    Other versions:
  21. Leigh TESFATSION, 1995. "A Trade Network Game With Endogenous Partner Selection," Economic Report 36, Iowa State University Department of Economics. [Downloadable!]
    Other versions:
  22. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January. [Downloadable!] (restricted)
  23. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002. [Downloadable!]
  24. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-37, February. [Downloadable!] (restricted)
  25. U. Witt, 2006. "Evolutionary Economics," Papers on Economics and Evolution 2006-05, Max Planck Institute of Economics, Evolutionary Economics Group.
  26. W. Brian Arthur & John H. Holland & Blake LeBaron & Richard Palmer & Paul Taylor, 1996. "Asset Pricing Under Endogenous Expectation in an Artificial Stock Market," Working Papers 96-12-093, Santa Fe Institute.
  27. Stanley, E.A. & Ashlock, D. & Tesfatsion, Leigh S., 2004. "Iterated Prisoner's Dilemma with Choice and Refusal of Partners," Staff General Research Papers 11180, Iowa State University, Department of Economics.
  28. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May. [Downloadable!] (restricted)
    Other versions:
  29. Birchenhall, Chris, 1995. "Modular Technical Change and Genetic Algorithms," Computational Economics, Springer, vol. 8(3), pages 233-53, August.
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