IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Bayesian Learning in UnstableSettings: Experimental Evidence Based on the Bandit Problem

  • Élise PAYZAN LE NESTOUR

    (Swiss Finance Institute at the École Polytechnique Fédérale de Lausanne (EPFL))

We study learning in a bandit problem where the outcome probabilities of six arms switch (jump) over time a restless bandit. In the experiment, optimal Bayesian learning tracks the jumps through learning of the probability of a jump or direct jump detection and, once a jump has occurred, re-learns the outcome probabilities. Such Bayesian learning is much more complex than the natural alternative which learns through trial-and-error (adaptive expectations). Yet, when combined with a partially myopic decision rule, Bayesian learning better matches the behavior observed in the lab. This result suggests that agents may be less limited in their computational capacities than previously thought, and that complexity does not always hamper fully rational learning.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://ssrn.com/abstract=1628657
Download Restriction: no

File URL:
Download Restriction: no

Paper provided by Swiss Finance Institute in its series Swiss Finance Institute Research Paper Series with number 10-28.

as
in new window

Length: 23 pages
Date of creation: Jun 2010
Date of revision:
Handle: RePEc:chf:rpseri:rp1028
Contact details of provider: Web page: http://www.SwissFinanceInstitute.ch
More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Ang, Andrew & Timmermann, Allan G, 2011. "Regime Changes and Financial Markets," CEPR Discussion Papers 8480, C.E.P.R. Discussion Papers.
  2. Guidolin, Massimo & Timmermann, Allan G, 2001. "Option Prices under Bayesian Learning: Implied Volatility Dynamics and Predictive Densities," CEPR Discussion Papers 3005, C.E.P.R. Discussion Papers.
  3. Sergiu Hart, 2004. "Adaptive Heuristics," Discussion Paper Series dp372, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
  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. 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 848-81, September.
  6. Pástor, Luboš & Veronesi, Pietro, 2009. "Learning in Financial Markets," CEPR Discussion Papers 7127, C.E.P.R. Discussion Papers.
  7. Wilcox, Nathaniel T, 1993. "Lottery Choice: Incentives, Complexity and Decision Time," Economic Journal, Royal Economic Society, vol. 103(421), pages 1397-1417, November.
  8. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
  9. Charness, Gary & Karni, Edi & Levin, Dan, 2010. "On the conjunction fallacy in probability judgment: New experimental evidence regarding Linda," Games and Economic Behavior, Elsevier, vol. 68(2), pages 551-556, March.
  10. Xavier Gabaix & David Laibson & Guillermo Moloche & Stephen Weinberg, 2006. "Costly Information Acquisition: Experimental Analysis of a Boundedly Rational Model," American Economic Review, American Economic Association, vol. 96(4), pages 1043-1068, September.
  11. Peter Klibanoff & Massimo Marinacci & Sujoy Mukerji, 2005. "A Smooth Model of Decision Making under Ambiguity," Econometrica, Econometric Society, vol. 73(6), pages 1849-1892, November.
  12. Machina,Mark & Schmeidler,David, 1991. "A more robust definition of subjective probability," Discussion Paper Serie A 365, University of Bonn, Germany.
  13. Gary Charness & Dan Levin, 2005. "When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect," American Economic Review, American Economic Association, vol. 95(4), pages 1300-1309, September.
  14. Jeffrey Banks & David Porter & Mark Olson, 1997. "An experimental analysis of the bandit problem," Economic Theory, Springer, vol. 10(1), pages 55-77.
  15. James J. Choi & David Laibson & Brigitte C. Madrian & Andrew Metrick, 2009. "Reinforcement Learning and Savings Behavior," Journal of Finance, American Finance Association, vol. 64(6), pages 2515-2534, December.
  16. Antoine J. Bruguier & Steven R. Quartz & Peter Bossaerts, 2010. "Exploring the Nature of "Trader Intuition"," Journal of Finance, American Finance Association, vol. 65(5), pages 1703-1723, October.
  17. Marcello Basili & Carlo Zappia, 2005. "Ambiguity and uncertainty in Ellsberg and Shackle," Department of Economics University of Siena 460, Department of Economics, University of Siena.
  18. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
  19. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
  20. Rothschild, Michael, 1974. "A two-armed bandit theory of market pricing," Journal of Economic Theory, Elsevier, vol. 9(2), pages 185-202, October.
  21. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
  22. Camelia Kuhnen & Brian Knutson, 2005. "The Neural Basis of Financial Risk Taking," Experimental 0509001, EconWPA.
  23. Epstein, Larry G. & Schneider, Martin, 2003. "Recursive multiple-priors," Journal of Economic Theory, Elsevier, vol. 113(1), pages 1-31, November.
  24. Parkes, David C. & Huberman, Bernardo A., 2001. "Multiagent Cooperative Search for Portfolio Selection," Games and Economic Behavior, Elsevier, vol. 35(1-2), pages 124-165, April.
  25. Bruce Ian Carlin & Gustavo Manso, 2011. "Obfuscation, Learning, and the Evolution of Investor Sophistication," Review of Financial Studies, Society for Financial Studies, vol. 24(3), pages 754-785.
  26. Ghirardato, Paolo & Marinacci, Massimo, 2002. "Ambiguity Made Precise: A Comparative Foundation," Journal of Economic Theory, Elsevier, vol. 102(2), pages 251-289, February.
  27. Cao, H. Henry & Han, Bing & Hirshleifer, David, 2011. "Taking the road less traveled by: Does conversation eradicate pernicious cascades?," Journal of Economic Theory, Elsevier, vol. 146(4), pages 1418-1436, July.
  28. Hirshleifer, David, 2001. "Investor Psychology and Asset Pricing," MPRA Paper 5300, University Library of Munich, Germany.
  29. Gur Huberman, 2001. "Contagious Speculation and a Cure for Cancer: A Nonevent that Made Stock Prices Soar," Journal of Finance, American Finance Association, vol. 56(1), pages 387-396, 02.
  30. Gustavo Manso, 2011. "Motivating Innovation," Journal of Finance, American Finance Association, vol. 66(5), pages 1823-1860, October.
  31. Hirshleifer, David & Lim, Sonya Seongyeon & Teoh, Siew Hong, 2006. "Driven to distraction: Extraneous events and underreaction to earnings news," MPRA Paper 3110, University Library of Munich, Germany, revised 16 Apr 2007.
  32. Stefano Dellavigna & Joshua M. Pollet, 2009. "Investor Inattention and Friday Earnings Announcements," Journal of Finance, American Finance Association, vol. 64(2), pages 709-749, 04.
  33. Timmermann, Allan G, 1993. "How Learning in Financial Markets Generates Excess Volatility and Predictability in Stock Prices," The Quarterly Journal of Economics, MIT Press, vol. 108(4), pages 1135-45, November.
  34. Carlin, Bruce I., 2009. "Strategic price complexity in retail financial markets," Journal of Financial Economics, Elsevier, vol. 91(3), pages 278-287, March.
  35. Andrew Caplin & Mark Dean, 2008. "Dopamine, Reward Prediction Error, and Economics," The Quarterly Journal of Economics, MIT Press, vol. 123(2), pages 663-701, 05.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:chf:rpseri:rp1028. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marilyn Barja)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.