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Scaling Up Learning Models in Public Good Games

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  • Jasmina Arifovic
  • John Ledyard

Abstract

We study three learning rules (reinforcement learning (RL), experience weighted attraction learning (EWA), and individual evolutionary learning (IEL)) and how they perform in three different Groves-Ledyard mechanisms. We are interested in how well these learning rules duplicate human behavior in repeated games with a continuum of strategies. We find that RL does not do well, IEL does significantly better, as does EWA, but only if given a small discretized strategy space. We identify four main features a learning rule should have in order to stack up against humans in a minimal competency test: (1) the use of hypotheticals to create history, (2) the ability to focus only on what is important, (3) the ability to forget history when it is no longer important, and (4) the ability to try new things. Copyright 2004 Blackwell Publishing Inc..

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Bibliographic Info

Article provided by Association for Public Economic Theory in its journal Journal of Public Economic Theory.

Volume (Year): 6 (2004)
Issue (Month): 2 (05)
Pages: 203-238

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Handle: RePEc:bla:jpbect:v:6:y:2004:i:2:p:203-238

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Cited by:
  1. Arifovic, Jasmina & Karaivanov, Alexander, 2010. "Learning by doing vs. learning from others in a principal-agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1967-1992, October.
  2. Juergen Bracht & Charles Figuières & Marisa Ratto, 2004. "Relative performance of two simple incentive mechanisms in a public good experiment," IDEP Working Papers 0409, Institut d'economie publique (IDEP), Marseille, France.
  3. Jasmina Arifovic & John Ledyard, 2012. "Individual Evolutionary Learning, Other-regarding Preferences, and the Voluntary Contributions Mechanism," Discussion Papers wp12-01, Department of Economics, Simon Fraser University.
  4. Jasmina Arifovic & Michael Maschek, 2006. "Revisiting Individual Evolutionary Learning in the Cobweb Model – An Illustration of the Virtual Spite-Effect," Computational Economics, Society for Computational Economics, vol. 28(4), pages 333-354, November.
  5. Brenner, Thomas, 2006. "Agent Learning Representation: Advice on Modelling Economic Learning," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 18, pages 895-947 Elsevier.
  6. Anufriev, M. & Arifovic, J. & Ledyard, D. & Panchenko, V., 2010. "Efficiency of Continuous Double Auctions under Individual Evolutionary Learning with Full or Limited Information," CeNDEF Working Papers 10-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  7. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.
  8. Juergen Bracht & Charles Figuieres & Marisa Ratto, 2008. "Relative performance of two simple incentive mechanisms in a public goods experiment," Working Papers 22970, Institut National de la Recherche Agronomique, France.
  9. Graupner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 135, Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO).
  10. Shu-Heng Chen & Chung-Ching Tai, 2006. "Republication: On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Society for Computational Economics, vol. 28(4), pages 313-331, November.
  11. Arifovic, Jasmina & Ledyard, John, 2012. "Individual evolutionary learning, other-regarding preferences, and the voluntary contributions mechanism," Journal of Public Economics, Elsevier, vol. 96(9-10), pages 808-823.
  12. Arifovic, Jasmina & Ledyard, John, 2011. "A behavioral model for mechanism design: Individual evolutionary learning," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 374-395, May.
  13. Erik Kimbrough, 2011. "Learning to respect property by refashioning theft into trade," Experimental Economics, Springer, vol. 14(1), pages 84-109, March.
  14. Arifovic, Jasmina & Ledyard, John, 2007. "Call market book information and efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1971-2000, June.
  15. Shu-Heng Chen & Chung-Ching Tai, 2006. "On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Society for Computational Economics, vol. 28(1), pages 51-69, August.
  16. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier.
  17. Dietrichson, Jens, 2013. "Coordination Incentives, Performance Measurement and Resource Allocation in Public Sector Organizations," Working Papers 2013:26, Lund University, Department of Economics.
  18. Kimbrough, Erik O., 2011. "Heuristic learning and the discovery of specialization and exchange," Journal of Economic Dynamics and Control, Elsevier, vol. 35(4), pages 491-511, April.

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