IDEAS home Printed from https://ideas.repec.org/p/ecl/stabus/3060.html
   My bibliography  Save this paper

Choosing a Good Toolkit: An Essay in Behavioral Economics

Author

Listed:
  • Kreps, David M.

    (Stanford University)

  • Francetich, Alejandro

Abstract

The problemof choosing an optimal toolkit day after day,when there is uncertainty concerning the value of different tools that can only be resolved by carrying the tools, is a multi-armed bandit problem with nonindependent arms. Accordingly, except for very simple specifications, this optimization problem cannot (practically) be solved. Decision takers facing this problem presumably resort to decision heuristics, “sensible” rules fordeciding which tools to carry, based on past experience. In this paper, we examine and compare the performance of a variety of heuristics, some very simple and others inspired by the computer-science literature on these problems. Some asymptotic results are obtained, especially concerning the long-run outcomes of using the heuristics, hence these results indicate which heuristics do well when the discount factor is close to one. But our focus is on the relative performance of these heuristics for discount factors bounded away from one, which we study through simulation of the heuristics on a collection of test problems.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Kreps, David M. & Francetich, Alejandro, 2014. "Choosing a Good Toolkit: An Essay in Behavioral Economics," Research Papers 3060, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3060
    as

    Download full text from publisher

    File URL: http://www.gsb.stanford.edu/faculty-research/working-papers/choosing-good-toolkit-essay-behavioral-economics
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Milgrom, Paul & Roberts, John, 1991. "Adaptive and sophisticated learning in normal form games," Games and Economic Behavior, Elsevier, vol. 3(1), pages 82-100, February.
    2. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    3. Blume, Lawrence E. & Easley, David, 1982. "Learning to be rational," Journal of Economic Theory, Elsevier, vol. 26(2), pages 340-351, April.
    4. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
    5. Kreps, David M. & Francetich, Alejandro, 2014. "Bayesian Inference Does Not Lead You Astray . . . On Average," Research Papers 3059, Stanford University, Graduate School of Business.
    6. Bray, Margaret, 1982. "Learning, estimation, and the stability of rational expectations," Journal of Economic Theory, Elsevier, vol. 26(2), pages 318-339, April.
    7. Francetich, Alejandro & Kreps, David, 2014. "Bayesian inference does not lead you astray…on average," Economics Letters, Elsevier, vol. 125(3), pages 444-446.
    8. Alejandro Francetich, 2014. "Managing Multiple Research Projects," Working Papers 516, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Simon, Herbert A, 1979. "Rational Decision Making in Business Organizations," American Economic Review, American Economic Association, vol. 69(4), pages 493-513, September.
    10. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francetich, Alejandro & Kreps, David, 2014. "Bayesian inference does not lead you astray…on average," Economics Letters, Elsevier, vol. 125(3), pages 444-446.

    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. Cho, In-Koo, 2005. "Introduction to learning and bounded rationality," Journal of Economic Theory, Elsevier, vol. 124(2), pages 127-128, October.
    2. Shurojit Chatterji & Ignacio N. Lobato, 2010. "Transformations of the state variable and learning dynamics," International Journal of Economic Theory, The International Society for Economic Theory, vol. 6(4), pages 385-403, December.
    3. Ignacio Esponda & Demian Pouzo, 2014. "Berk-Nash Equilibrium: A Framework for Modeling Agents with Misspecified Models," Papers 1411.1152, arXiv.org, revised Nov 2019.
    4. Kopányi, Dávid, 2017. "The coexistence of stable equilibria under least squares learning," Journal of Economic Behavior & Organization, Elsevier, vol. 141(C), pages 277-300.
    5. Siegfried Berninghaus & Werner Güth & M. Vittoria Levati & Jianying Qiu, 2006. "Satisficing in sales competition: experimental evidence," Papers on Strategic Interaction 2006-32, Max Planck Institute of Economics, Strategic Interaction Group.
    6. Guidolin, Massimo & Timmermann, Allan, 2007. "Properties of equilibrium asset prices under alternative learning schemes," Journal of Economic Dynamics and Control, Elsevier, vol. 31(1), pages 161-217, January.
    7. David Cooper & John Kagel, 2008. "Learning and transfer in signaling games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 34(3), pages 415-439, March.
    8. Xie, Erhao, 2021. "Empirical properties and identification of adaptive learning models in behavioral game theory," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 798-821.
    9. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    10. Kyle Hyndman & Antoine Terracol & Jonathan Vaksmann, 2009. "Learning and sophistication in coordination games," Experimental Economics, Springer;Economic Science Association, vol. 12(4), pages 450-472, December.
    11. Thomas J. Sargent & Noah Williams, 2005. "Impacts of Priors on Convergence and Escapes from Nash Inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 360-391, April.
    12. Ewerhart, Christian & Valkanova, Kremena, 2020. "Fictitious play in networks," Games and Economic Behavior, Elsevier, vol. 123(C), pages 182-206.
    13. 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.
    14. Victor Aguirregabiria & Jihye Jeon, 2020. "Firms’ Beliefs and Learning: Models, Identification, and Empirical Evidence," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(2), pages 203-235, March.
    15. Salle, Isabelle & Seppecher, Pascal, 2016. "Social Learning About Consumption," Macroeconomic Dynamics, Cambridge University Press, vol. 20(7), pages 1795-1825, October.
    16. repec:tcd:wpaper:tep6 is not listed on IDEAS
    17. repec:wvu:wpaper:10-18 is not listed on IDEAS
    18. Athanasios Orphanides & John Williams, 2004. "Imperfect Knowledge, Inflation Expectations, and Monetary Policy," NBER Chapters, in: The Inflation-Targeting Debate, National Bureau of Economic Research, Inc.
    19. Yan Chen & Robert Gazzale, 2004. "When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting," American Economic Review, American Economic Association, vol. 94(5), pages 1505-1535, December.
    20. Ulrich Berger, 2003. "A general model of best response adaptation," Game Theory and Information 0303008, University Library of Munich, Germany.
    21. Hofbauer,J. & Sandholm,W.H., 2001. "Evolution and learning in games with randomly disturbed payoffs," Working papers 5, Wisconsin Madison - Social Systems.
    22. Thomas Norman, 2012. "Learning Within Rational-Expectations Equilibrium," Economics Series Working Papers 591, University of Oxford, Department of Economics.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ecl:stabus:3060. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/gsstaus.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.