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

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Author Info
John Duffy
Ed Hopkins ()

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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.

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Paper provided by Edinburgh School of Economics, University of Edinburgh in its series ESE Discussion Papers with number 78.

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Length: 31
Date of creation: Mar 2004
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Handle: RePEc:edn:esedps:78

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  1. Rami Zwick & Amnon Rapoport, 2002. "Tacit Coordination in a Decentralized Market Entry Game with Fixed Capacity," Experimental Economics, Springer, vol. 5(3), pages 253-272, December. [Downloadable!] (restricted)
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  2. Giulio Bottazzi & Giovanna Devetag, 2002. "Coordination and Self-Organization in Minority Games: Experimental Evidence," ROCK Working Papers 019, Department of Computer and Management Sciences, University of Trento, Italy, revised 12 Jun 2008. [Downloadable!]
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  3. Giovanna Devetag, 2000. "Coordination in "Critical Mass" Games: An Experimental Study," LEM Papers Series 2000/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
  4. Ed Hopkins & Martin Posch, 2003. "Attainability of Boundary Points under Reinforcement Learning," Levine's Bibliography 506439000000000350, UCLA Department of Economics. [Downloadable!]
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  5. Dindo, P.D.E. & Tuinstra, J., 2006. "A Behavioral Model for Participation Games with Negative Feedback," CeNDEF Working Papers 06-10, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance. [Downloadable!]
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  6. Huck, Steffen & Ruchala, Gabriele K. & Tyran, Jean-Robert, 2006. "Competition Fosters Trust," CEPR Discussion Papers 6009, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  7. Duncan Whitehead, 2008. "The El Farol Bar Problem Revisited: Reinforcement Learning in a Potential Game," ESE Discussion Papers 186, Edinburgh School of Economics, University of Edinburgh. [Downloadable!]
  8. Laurent Denant-Boemont & Sabrina Hammiche, 2009. "Public Transit Capacity and User’s Choice: AnExperiment on Downs-Thomson Paradox," Working Papers halshs-00405501_v1, HAL. [Downloadable!]
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  9. Giovanna Devetag, 2003. "Coordination and Information in Critical Mass Games: An Experimental Study," Experimental Economics, Springer, vol. 6(1), pages 53-73, June. [Downloadable!] (restricted)
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  10. Giulio Bottazzi & Giovanna Devetag, 2007. "Competition and coordination in experimental minority games," Journal of Evolutionary Economics, Springer, vol. 17(3), pages 241-275, June. [Downloadable!] (restricted)
  11. Ed Hopkins, 2004. "Two Competing Models of How People Learn in Games," ESE Discussion Papers 51, Edinburgh School of Economics, University of Edinburgh. [Downloadable!]
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  12. John Duffy, 2004. "Agent-Based Models and Human Subject Experiments," Computational Economics 0412001, EconWPA. [Downloadable!]
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  13. Josef Hofbauer & Ed Hopkins, 2004. "Learning in Perturbed Asymmetric Games," ESE Discussion Papers 53, Edinburgh School of Economics, University of Edinburgh. [Downloadable!]
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