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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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  • Jasmina Arifovic & John Ledyard, 2004. "Scaling Up Learning Models in Public Good Games," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 6(2), pages 203-238, May.
  • Handle: RePEc:bla:jpbect:v:6:y:2004:i:2:p:203-238
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    Citations

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    Cited by:

    1. Philippe Aghion & Ernst Fehr & Richard Holden & Tom Wilkening, 2015. "The Role of Bounded Rationality and Imperfect Information in Subgame Perfect Implementation - An Empirical Investigation," CESifo Working Paper Series 5300, CESifo Group Munich.
    2. Bracht, Juergen & Figuieres, Charles & Ratto, Marisa, 2008. "Relative performance of two simple incentive mechanisms in a public goods experiment," Journal of Public Economics, Elsevier, vol. 92(1-2), pages 54-90, February.
    3. Graupner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)
      [Das räumliche agenten-basierte Wettbewerbsmodell SpAbCoM]
      ," IAMO Discussion Papers 135, Leibniz Institute of Agricultural Development in Transition Economies (IAMO).
    4. Erik Kimbrough, 2011. "Learning to respect property by refashioning theft into trade," Experimental Economics, Springer;Economic Science Association, vol. 14(1), pages 84-109, March.
    5. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Dietrichson, Jens, 2013. "Coordination Incentives, Performance Measurement and Resource Allocation in Public Sector Organizations," Working Papers 2013:26, Lund University, Department of Economics.
    12. 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.
    13. 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.
    14. Jasmina Arifovic & Michael Maschek, 2006. "Revisiting Individual Evolutionary Learning in the Cobweb Model – An Illustration of the Virtual Spite-Effect," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 333-354, November.
    15. 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.
    16. Shu-Heng Chen & Chung-Ching Tai, 2006. "Republication: On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 313-331, November.
    17. Amado, André & Huang, Weini & Campos, Paulo R.A. & Ferreira, Fernando Fagundes, 2015. "Learning process in public goods games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 21-31.
    18. 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.
    19. Alejandro Lee-Penagos, 2016. "Modelling Contributions in Public Good Games with Punishment," Discussion Papers 2016-15, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
    20. Shu-Heng Chen & Chung-Ching Tai, 2006. "On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 51-69, August.

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