IDEAS home Printed from https://ideas.repec.org/a/bla/jpbect/v6y2004i2p203-238.html
   My bibliography  Save this article

Scaling Up Learning Models in Public Good Games

Author

Listed:
  • 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.

Suggested Citation

  • 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
    DOI: 10.1111/j.1467-9779.2004.00165.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9779.2004.00165.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9779.2004.00165.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Groves, Theodore & Ledyard, John O, 1977. "Optimal Allocation of Public Goods: A Solution to the "Free Rider" Problem," Econometrica, Econometric Society, vol. 45(4), pages 783-809, May.
    2. Chen, Yan & Plott, Charles R., 1996. "The Groves-Ledyard mechanism: An experimental study of institutional design," Journal of Public Economics, Elsevier, vol. 59(3), pages 335-364, March.
    3. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    4. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    5. Josephson, Jens, 2008. "A numerical analysis of the evolutionary stability of learning rules," Journal of Economic Dynamics and Control, Elsevier, vol. 32(5), pages 1569-1599, May.
    6. 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 848-881, September.
    7. Yan Chen & Fang-Fang Tang, 1998. "Learning and Incentive-Compatible Mechanisms for Public Goods Provision: An Experimental Study," Journal of Political Economy, University of Chicago Press, vol. 106(3), pages 633-662, June.
    8. Arifovic, Jasmina & McKelvey, Richard D. & Pevnitskaya, Svetlana, 2006. "An initial implementation of the Turing tournament to learning in repeated two-person games," Games and Economic Behavior, Elsevier, vol. 57(1), pages 93-122, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cason, Timothy N. & Saijo, Tatsuyoshi & Yamato, Takehiko & Yokotani, Konomu, 2004. "Non-excludable public good experiments," Games and Economic Behavior, Elsevier, vol. 49(1), pages 81-102, October.
    2. C. Monica Capra & Jacob K Goeree & Rosario Gomez & Charles A Holt, 2002. "Learning and Noisy Equilibrium Behavior in an Experimental Study of Imperfect Price Competition," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(3), pages 613-636, August.
    3. Ho, Teck H. & Camerer, Colin F. & Chong, Juin-Kuan, 2007. "Self-tuning experience weighted attraction learning in games," Journal of Economic Theory, Elsevier, vol. 133(1), pages 177-198, March.
    4. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.
    5. Hanaki, Nobuyuki & Ishikawa, Ryuichiro & Akiyama, Eizo, 2009. "Learning games," Journal of Economic Dynamics and Control, Elsevier, vol. 33(10), pages 1739-1756, October.
    6. Chen, Yan & Khoroshilov, Yuri, 2003. "Learning under limited information," Games and Economic Behavior, Elsevier, vol. 44(1), pages 1-25, July.
    7. John Ledyard & Jasmina Arifovic, 2003. "Computer Testbeds: The Dynamics of Groves-Ledyard Mechanisms," Computing in Economics and Finance 2003 244, Society for Computational Economics.
    8. Schuster, Stephan, 2010. "Network Formation with Adaptive Agents," MPRA Paper 27388, University Library of Munich, Germany.
    9. 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.
    10. Ido Erev & Eyal Ert & Alvin E. Roth, 2010. "A Choice Prediction Competition for Market Entry Games: An Introduction," Games, MDPI, vol. 1(2), pages 1-20, May.
    11. Chernov, G. & Susin, I., 2019. "Models of learning in games: An overview," Journal of the New Economic Association, New Economic Association, vol. 44(4), pages 77-125.
    12. Tatsuyoshi Saijo & Takehiko Yamato & Konomu Yokotani, 2003. "Non-Excludable Public Good Experiments revised October 2003, forthcoming in Games and Economic Behavior," Discussion papers 03011, Research Institute of Economy, Trade and Industry (RIETI).
    13. Teck H Ho & Colin Camerer & Juin-Kuan Chong, 2003. "Functional EWA: A one-parameter theory of learning in games," Levine's Working Paper Archive 506439000000000514, David K. Levine.
    14. repec:dpr:wpaper:0874 is not listed on IDEAS
    15. Ianni, A., 2002. "Reinforcement learning and the power law of practice: some analytical results," Discussion Paper Series In Economics And Econometrics 203, Economics Division, School of Social Sciences, University of Southampton.
    16. Sergiu Hart & Andreu Mas-Colell, 2013. "A Simple Adaptive Procedure Leading To Correlated Equilibrium," World Scientific Book Chapters, in: Simple Adaptive Strategies From Regret-Matching to Uncoupled Dynamics, chapter 2, pages 17-46, World Scientific Publishing Co. Pte. Ltd..
    17. Marco LiCalzi & Roland Mühlenbernd, 2022. "Feature-weighted categorized play across symmetric games," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 1052-1078, June.
    18. Osili, Una Okonkwo & Paulson, Anna, 2014. "Crises and confidence: Systemic banking crises and depositor behavior," Journal of Financial Economics, Elsevier, vol. 111(3), pages 646-660.
    19. Christos A. Ioannou & Julian Romero, 2012. "Strategic Learning With Finite Automata Via The EWA-Lite Model," Purdue University Economics Working Papers 1269, Purdue University, Department of Economics.
    20. Rottenstreich, Yuval & Kivetz, Ran, 2006. "On decision making without likelihood judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(1), pages 74-88, September.
    21. Nobuyuki Hanaki, 2007. "Individual and Social Learning," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 421-421, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jpbect:v:6:y:2004:i:2:p:203-238. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/apettea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.