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Learning, Information and Sorting in Market Entry Games: Theory and Evidence

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Abstract

Previous data from experiments on market entry games, N-player games where each player faces a choice between entering a market and staying out, appear inconsistent with either mixed or pure Nash equilibria. Here we show that, in this class of game, learning theory predicts sorting, that is, in the long run, agents play a pure strategy equilibrium with some agents permanently in the market, and some permanently out. We conduct experiments with a larger number of repetitions than in previous work in order to test this prediction. We find that when subjects are given minimal information, only after close to 100 periods do subjects begin to approach equilibrium. In contrast, with full information, subjects learn to play a pure strategy equilibrium relatively quickly. However, the information which permits rapid convergence, revelation of the individual play of all opponents, is not predicted to have any effect by existing models of learning.

Suggested Citation

  • John Duffy & Ed Hopkins, 2001. "Learning, Information and Sorting in Market Entry Games: Theory and Evidence," Edinburgh School of Economics Discussion Paper Series 78, Edinburgh School of Economics, University of Edinburgh.
    • Handle: RePEc:edn:esedps:78
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    1. Christopher M. Anderson & Colin F. Camerer, 2000. "Experience-weighted attraction learning in sender-receiver signaling games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 16(3), pages 689-718.
    2. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    3. 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.
    4. Amnon Rapoport & Darryl A. Seale & Ido Erev & James A. Sundali, 1998. "Equilibrium Play in Large Group Market Entry Games," Management Science, INFORMS, vol. 44(1), pages 119-141, January.
    5. Ed Hopkins, 2002. "Two Competing Models of How People Learn in Games," Econometrica, Econometric Society, vol. 70(6), pages 2141-2166, November.
    6. Hopkins, Ed & Posch, Martin, 2005. "Attainability of boundary points under reinforcement learning," Games and Economic Behavior, Elsevier, vol. 53(1), pages 110-125, October.
    7. Erev, Ido & Rapoport, Amnon, 1998. "Coordination, "Magic," and Reinforcement Learning in a Market Entry Game," Games and Economic Behavior, Elsevier, vol. 23(2), pages 146-175, May.
    8. Sundali, James A. & Rapoport, Amnon & Seale, Darryl A., 1995. "Coordination in Market Entry Games with Symmetric Players," Organizational Behavior and Human Decision Processes, Elsevier, vol. 64(2), pages 203-218, November.
    9. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    10. Rapoport, Amnon & Seale, Darryl A. & Winter, Eyal, 2002. "Coordination and Learning Behavior in Large Groups with Asymmetric Players," Games and Economic Behavior, Elsevier, vol. 39(1), pages 111-136, April.
    11. Hofbauer, Josef & Hopkins, Ed, 2005. "Learning in perturbed asymmetric games," Games and Economic Behavior, Elsevier, vol. 52(1), pages 133-152, July.
    12. Darryl A. Seale & Amnon Rapoport, 2000. "Elicitation of Strategy Profiles in Large Group Coordination Games," Experimental Economics, Springer;Economic Science Association, vol. 3(2), pages 153-179, October.
    13. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    14. Fernando Vega-Redondo, 1997. "The Evolution of Walrasian Behavior," Econometrica, Econometric Society, vol. 65(2), pages 375-384, March.
    15. Benaim, Michel & Hirsch, Morris W., 1999. "Mixed Equilibria and Dynamical Systems Arising from Fictitious Play in Perturbed Games," Games and Economic Behavior, Elsevier, vol. 29(1-2), pages 36-72, October.
    16. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    17. Darryl Seale & Amnon Rapoport, 2000. "Elicitation of Strategy Profiles in Large Group Coordination Games," Experimental Economics, Springer;Economic Science Association, vol. 3(2), pages 153-179, October.
    18. Camerer, Colin F. & Ho, Teck-Hua & Chong, Juin-Kuan, 2002. "Sophisticated Experience-Weighted Attraction Learning and Strategic Teaching in Repeated Games," Journal of Economic Theory, Elsevier, vol. 104(1), pages 137-188, May.
    19. Dan Lovallo & Colin Camerer, 1999. "Overconfidence and Excess Entry: An Experimental Approach," American Economic Review, American Economic Association, vol. 89(1), pages 306-318, March.
    20. Eyal Winter & Amnon Rapoport & Darryl A. Seale, 2000. "An experimental study of coordination and learning in iterated two-market entry games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 16(3), pages 661-687.
    21. Sarin, Rajiv & Vahid, Farshid, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 28(2), pages 294-309, August.
    22. Monderer, Dov & Shapley, Lloyd S., 1996. "Fictitious Play Property for Games with Identical Interests," Journal of Economic Theory, Elsevier, vol. 68(1), pages 258-265, January.
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