IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Agent-Based Computational Economics

  • Leigh Tesfatsion

    (Iowa State University)

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://128.118.178.162/eps/comp/papers/0203/0203001.pdf
Download Restriction: no

Paper provided by EconWPA in its series Computational Economics with number 0203001.

as
in new window

Length: 30 pages
Date of creation: 15 Mar 2002
Date of revision: 15 Aug 2002
Handle: RePEc:wpa:wuwpco:0203001
Note: Type of Document - Acrobat PDF; prepared on IBM PC - PC-TEX; to print on HP/PostScript/; pages: 30; figures: None
Contact details of provider: Web page: http://128.118.178.162

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
  2. Hommes, C.H., 2001. "Modeling the stylized facts in finance through simple nonlinear adaptive systems," CeNDEF Working Papers 01-06, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  3. Robert E. Marks, . "Evolved Perception and Behaviour in Oligopolies," Computing in Economics and Finance 1996 _038, Society for Computational Economics.
  4. Elinor Ostrom, 2000. "Collective Action and the Evolution of Social Norms," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 137-158, Summer.
  5. repec:att:wimass:9010 is not listed on IDEAS
  6. Alvin E. Roth & Axel Ockenfels, . "Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet," Papers on Strategic Interaction 2002-32, Max Planck Institute of Economics, Strategic Interaction Group.
  7. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
  8. Leigh TESFATSION, 1995. "How Economists Can Get Alife," Economic Report 37, Iowa State University Department of Economics.
  9. 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.
  10. Nicolaisen, James & Petrov, Valentin & Tesfatsion, Leigh S., 2000. "Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing," Staff General Research Papers 1952, Iowa State University, Department of Economics.
  11. Krugman, Paul, 1994. "Complex Landscapes in Economic Geography," American Economic Review, American Economic Association, vol. 84(2), pages 412-16, May.
  12. Vriend, Nicolaas J, 1995. "Self-Organization of Markets: An Example of a Computational Approach," Computational Economics, Society for Computational Economics, vol. 8(3), pages 205-31, August.
  13. Klos, Tomas B. & Nooteboom, Bart, 2001. "Agent-based computational transaction cost economics," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 503-526, March.
  14. Leigh TESFATSION, 1995. "A Trade Network Game With Endogenous Partner Selection," Economic Report 36, Iowa State University Department of Economics.
  15. Weisbuch, G. & Kirman, A.P. & Herreiner, D., 1996. "Market Organisation," G.R.E.Q.A.M. 96a20, Universite Aix-Marseille III.
  16. Andersson, Martin R. & Sandholm, Tuomas W., 2001. "Leveled commitment contracts with myopic and strategic agents," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 615-640, March.
  17. Michael Prietula & Kathleen Carley & Les Gasser (ed.), 1998. "Simulating Organizations: Computational Models of Institutions and Groups," MIT Press Books, The MIT Press, edition 1, volume 1, number 026266108x, June.
  18. 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.
  19. Tay, Nicholas S. P. & Linn, Scott C., 2001. "Fuzzy inductive reasoning, expectation formation and the behavior of security prices," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 321-361, March.
  20. Bower, John & Bunn, Derek, 2001. "Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 561-592, March.
  21. Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers 5075, Iowa State University, Department of Economics.
  22. Duffy, John, 2001. "Learning to speculate: Experiments with artificial and real agents," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 295-319, March.
  23. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, June.
  24. Tesfatsion, Leigh, 1998. "Preferential Partner Selection in Evolutionary Labor Markets: A Study in Agent-Based Computational Economics," Staff General Research Papers 4063, Iowa State University, Department of Economics.
  25. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
  26. David McFadzean & Deron Stewart & Leigh Tesfatsion, 2000. "A Computational Laboratory for Evolutionary Trade Networks," Computational Economics 0004004, EconWPA.
  27. Rust, John & Miller, John H. & Palmer, Richard, 1994. "Characterizing effective trading strategies : Insights from a computerized double auction tournament," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 61-96, January.
  28. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-41, June.
  29. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, June.
  30. Marks, R E, 1992. "Breeding Hybrid Strategies: Optimal Behaviour for Oligopolists," Journal of Evolutionary Economics, Springer, vol. 2(1), pages 17-38, March.
  31. repec:att:wimass:9625 is not listed on IDEAS
  32. Arifovic, J. & Eaton, C., 1994. "Coordination via Genetic Learning," Discussion Papers dp94-11, Department of Economics, Simon Fraser University.
  33. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
  34. J. Doyne Farmer & Andrew W. Lo, 1999. "Frontiers of Finance: Evolution and Efficient Markets," Working Papers 99-06-039, Santa Fe Institute.
  35. Francesco Luna, . "Computable Learning, Neural Networks and Institutions," Computing in Economics and Finance 1996 _037, Society for Computational Economics.
  36. De Vany, Arthur & Lee, Cassey, 2001. "Quality signals in information cascades and the dynamics of the distribution of motion picture box office revenues," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 593-614, March.
  37. Robert Axtell & Robert Axelrod & Joshua M. Epstein & Michael D. Cohen, 1995. "Aligning Simulation Models: A Case Study and Results," Working Papers 95-07-065, Santa Fe Institute.
  38. Howitt, Peter & Clower, Robert, 2000. "The emergence of economic organization," Journal of Economic Behavior & Organization, Elsevier, vol. 41(1), pages 55-84, January.
  39. Axtell, R. & Epstein, J.M. & Young, H.P., 2000. "The Emergence of Classes in a Multi-Agent Bargaining Model," Papers 9, Brookings Institution - Working Papers.
  40. Kirman, Alan P. & Vriend, Nicolaas J., 2001. "Evolving market structure: An ACE model of price dispersion and loyalty," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 459-502, March.
  41. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
  42. 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.
  43. Epstein, Joshua M, 2001. "Learning to Be Thoughtless: Social Norms and Individual Computation," Computational Economics, Society for Computational Economics, vol. 18(1), pages 9-24, August.
  44. Lux, T. & M. Marchesi, . "Volatility Clustering in Financial Markets: A Micro-Simulation of Interacting Agents," Discussion Paper Serie B 437, University of Bonn, Germany, revised Jul 1998.
  45. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpco:0203001. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.