IDEAS home Printed from https://ideas.repec.org/a/gam/jgames/v2y2011i1p136-162d11718.html
   My bibliography  Save this article

A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition

Author

Listed:
  • Cleotilde Gonzalez

    (Dynamic Decision Making Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

  • Varun Dutt

    (Dynamic Decision Making Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

  • Tomás Lejarraga

    (Dynamic Decision Making Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

Abstract

This paper presents a case of parsimony and generalization in model comparisons. We submitted two versions of the same cognitive model to the Market Entry Competition (MEC), which involved four-person and two-alternative (enter or stay out) games. Our model was designed according to the Instance-Based Learning Theory (IBLT). The two versions of the model assumed the same cognitive principles of decision making and learning in the MEC. The only difference between the two models was the assumption of homogeneity among the four participants: one model assumed homogeneous participants (IBL-same) while the other model assumed heterogeneous participants (IBL-different). The IBL-same model involved three free parameters in total while the IBL-different involved 12 free parameters, i.e. , three free parameters for each of the four participants. The IBL-different model outperformed the IBL-same model in the competition, but after exposing the models to a more challenging generalization test (the Technion Prediction Tournament), the IBL-same model outperformed the IBL-different model. Thus, a loser can be a winner depending on the generalization conditions used to compare models. We describe the models and the process by which we reach these conclusions.

Suggested Citation

  • 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, vol. 2(1), pages 1-27, March.
    • Handle: RePEc:gam:jgames:v:2:y:2011:i:1:p:136-162:d:11718
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-4336/2/1/136/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-4336/2/1/136/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ido Erev & Eyal Ert & Alvin E. Roth, 2010. "Erev, I. et al . A Choice Prediction Competition for Market Entry Games: An Introduction. Games 2010, 1 , 117-136," Games, MDPI, vol. 1(3), pages 1-5, July.
    2. Pavlo Blavatsky, 2003. "Note on "Small Feedback-based Decisions and Their Limited Correspondence to Description-based Decisions"," CERGE-EI Working Papers wp218, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    3. Rapoport, Amnon & Erev, Ido & Abraham, Elizabeth V. & Olson, David E., 1997. "Randomization and Adaptive Learning in a Simplified Poker Game," Organizational Behavior and Human Decision Processes, Elsevier, vol. 69(1), pages 31-49, January.
    4. 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.
    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. 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, vol. 2(1), pages 1-13, March.
    2. Ofir Yakobi & Doron Cohen & Eitan Naveh & Ido Erev, 2020. "Reliance on small samples and the value of taxing reckless behaviors," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(2), pages 266-281, March.
    3. Lindner, Florian, 2014. "Decision time and steps of reasoning in a competitive market entry game," Economics Letters, Elsevier, vol. 122(1), pages 7-11.
    4. 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.
    5. Naoki Watanabe, 2022. "Reconsidering Meaningful Learning in a Bandit Experiment on Weighted Voting: Subjects’ Search Behavior," The Review of Socionetwork Strategies, Springer, vol. 16(1), pages 81-107, April.
    6. Olli Lappalainen, 2018. "Cooperation and Strategic Complementarity: An Experiment with Two Voluntary Contribution Mechanism Games with Interior Equilibria," Games, MDPI, vol. 9(3), pages 1-24, July.
    7. Ori Plonsky & Yefim Roth & Ido Erev, 2021. "Underweighting of rare events in social interactions and its implications to the design of voluntary health applications," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 16(2), pages 267-289, March.
    8. Andrea Sorensen, 2018. "Creating a Domain of Losses in the Laboratory: Effects of Endowment Size," Games, MDPI, vol. 9(1), pages 1-14, March.
    9. repec:cup:judgdm:v:12:y:2017:i:6:p:553-562 is not listed on IDEAS
    10. Silvia Angerer & E. Glenn Dutcher & Daniela Glätzle-Rützler & Philipp Lergetporer & Matthias Sutter, 2021. "The Formation of Risk Preferences Through Small-Scale Events," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2021_16, Max Planck Institute for Research on Collective Goods.
    11. 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.
    12. repec:cup:judgdm:v:16:y:2021:i:2:p:267-289 is not listed on IDEAS
    13. repec:cup:judgdm:v:8:y:2013:i:3:p:214-235 is not listed on IDEAS
    14. Anna Dorfman & Yoella Bereby-Meyer & Simone Moran, 2013. "When Feeling Skillful Impairs Coordination in a Lottery Selection Task," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-6, June.
    15. Eyal Ert & Ido Erev & Alvin E. Roth, 2011. "A Choice Prediction Competition for Social Preferences in Simple Extensive Form Games: An Introduction," Games, MDPI, vol. 2(3), pages 1-20, July.
    16. Xiaoyang Long & Javad Nasiry & Yaozhong Wu, 2020. "A Behavioral Study on Abandonment Decisions in Multistage Projects," Management Science, INFORMS, vol. 66(5), pages 1999-2016, May.
    17. 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.
    18. Kirman, Alan P. & Laisney, François & Pezanis-Christou, Paul, 2018. "Exploration vs exploitation, impulse balance equilibrium, and a specification test for the El Farol bar problem," ZEW Discussion Papers 18-038, ZEW - Leibniz Centre for European Economic Research.
    19. 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.
    20. 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.
    21. Wang, Yongjie & Yao, Zhouzhou & Wang, Chao & Ren, Jiale & Chen, Qiao, 2020. "The impact of intelligent transportation points system based on Elo rating on emergence of cooperation at Y intersection," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    22. Luke Lindsay, 2011. "Correlated Individual Differences and Choice Prediction," Games, MDPI, vol. 2(1), pages 1-5, February.
    23. 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.

    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:gam:jgames:v:2:y:2011:i:1:p:136-162:d:11718. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.