IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-02d80010.html

Learning to deal with risk: what does reinforcement learning tell us about risk attitudes?

Author

Listed:
  • Albert Burgos

    (Universidad de Murcia)

Abstract

People are generally reluctant to accept risk. In particular, people overvalue sure gains, relative to outcomes which are merely probable. At the same time, people are also more willing to accept bets when payoffs involve losses rather than gains. I consider how far adaptive learning can go in explaining these phenomena. I report simulations in which adaptive learners of the kind studied in Roth & Erev (1995, 1998) and Borgers & Sarin (1997, 2000) deal with a problem of repeated choice under risk where alternatives differ by a mean preserving spread. The simulations show that adaptive learning induces (on average) risk averse choices. This learning bias is stronger for gains than for losses. Also, risk averse choices are much more likely when one of the alternatives is a certain prospect. The implications of a learning interpretation of risk taking are explored.

Suggested Citation

  • Albert Burgos, 2002. "Learning to deal with risk: what does reinforcement learning tell us about risk attitudes?," Economics Bulletin, AccessEcon, vol. 4(10), pages 1-13.
    • Handle: RePEc:ebl:ecbull:eb-02d80010
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/pubs/EB/2002/Volume4/EB-02D80010A.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Reinhard Selten & Abdolkarim Sadrieh & Klaus Abbink, 1999. "Money Does Not Induce Risk Neutral Behavior, but Binary Lotteries Do even Worse," Theory and Decision, Springer, vol. 46(3), pages 213-252, June.
    3. Borgers, Tilman & Sarin, Rajiv, 2000. "Naive Reinforcement Learning with Endogenous Aspirations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 921-950, November.
    4. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    5. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    6. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    7. Loomes, Graham, 1998. "Probabilities vs Money: A Test of Some Fundamental Assumptions about Rational Decision Making," Economic Journal, Royal Economic Society, vol. 108(447), pages 477-489, March.
    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. Johannes G. Jaspersen & Richard Peter, 2017. "Experiential Learning, Competitive Selection, and Downside Risk: A New Perspective on Managerial Risk Taking," Organization Science, INFORMS, vol. 28(5), pages 915-930, October.
    2. Oyarzun, Carlos & Sarin, Rajiv, 2012. "Mean and variance responsive learning," Games and Economic Behavior, Elsevier, vol. 75(2), pages 855-866.
    3. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.

    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. repec:ebl:ecbull:v:4:y:2002:i:10:p:1-13 is not listed on IDEAS
    2. Francisco Gomes & Michael Haliassos & Tarun Ramadorai, 2021. "Household Finance," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 919-1000, September.
    3. Masiliūnas, Aidas, 2023. "Learning in rent-seeking contests with payoff risk and foregone payoff information," Games and Economic Behavior, Elsevier, vol. 140(C), pages 50-72.
    4. Peter Wakker & Veronika Köbberling & Christiane Schwieren, 2007. "Prospect-theory’s Diminishing Sensitivity Versus Economics’ Intrinsic Utility of Money: How the Introduction of the Euro can be Used to Disentangle the Two Empirically," Theory and Decision, Springer, vol. 63(3), pages 205-231, November.
    5. Erev, Ido & Bereby-Meyer, Yoella & Roth, Alvin E., 1999. "The effect of adding a constant to all payoffs: experimental investigation, and implications for reinforcement learning models," Journal of Economic Behavior & Organization, Elsevier, vol. 39(1), pages 111-128, May.
    6. Fiore, Annamaria, 2009. "Experimental Economics: Some Methodological Notes," MPRA Paper 12498, University Library of Munich, Germany.
    7. Laurent Denant-Boemont & Olivier L’Haridon, 2013. "La rationalité à l'épreuve de l'économie comportementale," Revue française d'économie, Presses de Sciences-Po, vol. 0(2), pages 35-89.
    8. Misha Perepelitsa, 2019. "RPS(1) Preferences," Papers 1901.04995, arXiv.org, revised Feb 2019.
    9. Misha Perepelitsa, 2019. "A model of discrete choice based on reinforcement learning under short-term memory," Papers 1908.06133, arXiv.org.
    10. Jaspersen, Johannes G. & Montibeller, Gilberto, 2020. "On the learning patterns and adaptive behavior of terrorist organizations," European Journal of Operational Research, Elsevier, vol. 282(1), pages 221-234.
    11. Yaroslav Rosokha & Kenneth Younge, 2020. "Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 569-582, July.
    12. Yaron Azrieli & Christopher P. Chambers & Paul J. Healy, 2020. "Incentives in experiments with objective lotteries," Experimental Economics, Springer;Economic Science Association, vol. 23(1), pages 1-29, March.
    13. Thomas Kourouxous & Thomas Bauer, 2019. "Violations of dominance in decision-making," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 209-239, April.
    14. Gijs Kuilen & Peter Wakker, 2006. "Learning in the Allais paradox," Journal of Risk and Uncertainty, Springer, vol. 33(3), pages 155-164, December.
    15. Kirchkamp, Oliver & Oechssler, Joerg & Sofianos, Andis, 2021. "The Binary Lottery Procedure does not induce risk neutrality in the Holt & Laury and Eckel & Grossman tasks," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 348-369.
    16. Dennis A. V. Dittrich & Werner Güth & Martin G. Kocher & Paul Pezanis‐Christou, 2012. "Loss Aversion and Learning to Bid," Economica, London School of Economics and Political Science, vol. 79(314), pages 226-257, April.
    17. Lukas, Moritz & Nöth, Markus, 2021. "Interest rate fixation periods and reference points," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    18. Rivas, Javier, 2013. "Probability matching and reinforcement learning," Journal of Mathematical Economics, Elsevier, vol. 49(1), pages 17-21.
    19. Mengel, Friederike, 2012. "Learning across games," Games and Economic Behavior, Elsevier, vol. 74(2), pages 601-619.
    20. Shu-Heng Chen & Bin-Tzong Chie & Ying-Fang Kao & Ragupathy Venkatachalam, 2019. "Agent-Based Modeling of a Non-tâtonnement Process for the Scarf Economy: The Role of Learning," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 305-341, June.
    21. Gabriel Galand, 2009. "The Neutrality of Money Revisited with a Bottom-Up Approach: Decentralisation, Limited Information and Bounded Rationality," Computational Economics, Springer;Society for Computational Economics, vol. 33(4), pages 337-360, May.

    More about this item

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

    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:ebl:ecbull:eb-02d80010. 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: John P. Conley (email available below). General contact details of provider: .

    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.