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A Choice Prediction Competition for Market Entry Games: An Introduction

Author

Listed:
  • Ido Erev

    () (Max Wertheimer Minerva Center for Cognitive Studies, Faculty of Industrial Engineering and Management, Technion, Haifa 32000, Israel)

  • Eyal Ert

    () (Computer Laboratory for Experimental Research, Harvard Business School, Boston, MA, 02163, USA)

  • Alvin E. Roth

    () (Department of Economics, 308 Littauer, Harvard University, Cambridge, MA 02138, USA
    Harvard Business School, 441 Baker Library, Boston, MA 02163, USA)

Abstract

A choice prediction competition is organized that focuses on decisions from experience in market entry games (http://sites.google.com/site/gpredcomp/ and http://www.mdpi.com/si/games/predict-behavior/). The competition is based on two experiments: An estimation experiment, and a competition experiment. The two experiments use the same methods and subject pool, and examine games randomly selected from the same distribution. The current introductory paper presents the results of the estimation experiment, and clarifies the descriptive value of several baseline models. The experimental results reveal the robustness of eight behavioral tendencies that were documented in previous studies of market entry games and individual decisions from experience. The best baseline model (I-SAW) assumes reliance on small samples of experiences, and strong inertia when the recent results are not surprising. The competition experiment will be run in May 2010 (after the completion of this introduction), but they will not be revealed until September. To participate in the competition, researchers are asked to E-mail the organizers models (implemented in computer programs) that read the incentive structure as input, and derive the predicted behavior as an output. The submitted models will be ranked based on their prediction error. The winners of the competition will be invited to publish a paper that describes their model.

Suggested Citation

  • Ido Erev & Eyal Ert & Alvin E. Roth, 2010. "A Choice Prediction Competition for Market Entry Games: An Introduction," Games, MDPI, Open Access Journal, vol. 1(2), pages 1-20, May.
  • Handle: RePEc:gam:jgames:v:1:y:2010:i:2:p:117-136:d:8348
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    Citations

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    Cited by:

    1. Eyal Ert & Ido Erev & Alvin E. Roth, 2011. "A Choice Prediction Competition for Social Preferences in Simple Extensive Form Games: An Introduction," Games, MDPI, Open Access Journal, vol. 2(3), pages 1-20, July.
    2. Anufriev, Mikhail & Kopányi, Dávid & Tuinstra, Jan, 2013. "Learning cycles in Bertrand competition with differentiated commodities and competing learning rules," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2562-2581.
    3. Eric Guerci & Nobuyuki Hanaki & Naoki Watanabe, 2017. "Meaningful learning in weighted voting games: an experiment," Theory and Decision, Springer, vol. 83(1), pages 131-153, June.
    4. W Chen & Y Chen & D Levine, 2015. "A Unifying Learning Framework for Building Artificial Game-Playing Agents," Levine's Working Paper Archive 786969000000001002, David K. Levine.
    5. Ion Juvina & Christian Lebiere & Jolie M. Martin & Cleotilde Gonzalez, 2011. "Intergroup Prisoner’s Dilemma with Intragroup Power Dynamics," Games, MDPI, Open Access Journal, vol. 2(1), pages 1-31, February.
    6. Madiès, Thierry & Villeval, Marie Claire & Wasmer, Malgorzata, 2013. "Intergenerational attitudes towards strategic uncertainty and competition: A field experiment in a Swiss bank," European Economic Review, Elsevier, vol. 61(C), pages 153-168.
    7. Zion, Uri Ben & Erev, Ido & Haruvy, Ernan & Shavit, Tal, 2010. "Adaptive behavior leads to under-diversification," Journal of Economic Psychology, Elsevier, vol. 31(6), pages 985-995, December.
    8. Lindner, Florian, 2014. "Decision time and steps of reasoning in a competitive market entry game," Economics Letters, Elsevier, vol. 122(1), pages 7-11.
    9. Eric Guerci & Nobuyuki Hanaki & Naoki Watanabe, 2015. "Meaningful Learning in Weighted Voting Games: An Experiment," Working Papers halshs-01216244, HAL.
    10. Peysakhovich, Alexander & Naecker, Jeffrey, 2017. "Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 373-384.
    11. repec:hrv:faseco:32067416 is not listed on IDEAS
    12. Thorsten Chmura & Sebastian Goerg & Reinhard Selten, 2011. "Learning in experimental 2 x 2 games," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2011_26, Max Planck Institute for Research on Collective Goods.
    13. Wei Chen & Shu-Yu Liu & Chih-Han Chen & Yi-Shan Lee, 2011. "Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games," Games, MDPI, Open Access Journal, vol. 2(1), pages 1-13, March.
    14. Cleotilde Gonzalez & Varun Dutt & Tomás Lejarraga, 2011. "A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition," Games, MDPI, Open Access Journal, vol. 2(1), pages 1-27, March.
    15. repec:jdm:journl:v:12:y:2017:i:6:p:553-562 is not listed on IDEAS
    16. Ioannou, Christos A. & Romero, Julian, 2014. "A generalized approach to belief learning in repeated games," Games and Economic Behavior, Elsevier, vol. 87(C), pages 178-203.
    17. André Palma & Mohammed Abdellaoui & Giuseppe Attanasi & Moshe Ben-Akiva & Ido Erev & Helga Fehr-Duda & Dennis Fok & Craig Fox & Ralph Hertwig & Nathalie Picard & Peter Wakker & Joan Walker & Martin We, 2014. "Beware of black swans: Taking stock of the description–experience gap in decision under uncertainty," Marketing Letters, Springer, vol. 25(3), pages 269-280, September.
    18. Chmura, Thorsten & Goerg, Sebastian J. & Selten, Reinhard, 2012. "Learning in experimental 2×2 games," Games and Economic Behavior, Elsevier, vol. 76(1), pages 44-73.
    19. Eyal Ert & Ido Erev, 2013. "On the descriptive value of loss aversion in decisions under risk: Six clarifications," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 8(3), pages 214-235, May.
    20. repec:gam:jgames:v:9:y:2018:i:1:p:13-:d:134325 is not listed on IDEAS
    21. Luke Lindsay, 2011. "Correlated Individual Differences and Choice Prediction," Games, MDPI, Open Access Journal, vol. 2(1), pages 1-5, February.

    More about this item

    Keywords

    reinforcement learning; excess entry; recency; surprise triggers change; inertia;

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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