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Nonparametric Learning Rules from Bandit Experiments: The Eyes have it!

  • Yingyao Hu
  • Yutaka Kayaba
  • Matt Shum

How do people learn? We assess, in a distribution-free manner, subjects?learning and choice rules in dynamic two-armed bandit (probabilistic reversal learning) experiments. To aid in identification and estimation, we use auxiliary measures of subjects?beliefs, in the form of their eye-movements during the experiment. Our estimated choice probabilities and learning rules have some distinctive features; notably that subjects tend to update in a non-smooth manner following choices made in accordance with current beliefs. Moreover, the beliefs implied by our nonparametric learning rules are closer to those from a (non-Bayesian) reinforcement learning model, than a Bayesian learning model.

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Paper provided by The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 560.

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Date of creation: Jun 2010
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Handle: RePEc:jhu:papers:560
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  1. Miguel A. Costa-Gomes & Vincent P. Crawford, 2004. "Cognition and Behavior in Two-Person Guessing Games: An Experimental Study," Levine's Bibliography 122247000000000113, UCLA Department of Economics.
  2. Vincent P. Crawford & Nagore Iriberri, 2007. "Level-k Auctions: Can a Nonequilibrium Model of Strategic Thinking Explain the Winner's Curse and Overbidding in Private-Value Auctions?," Econometrica, Econometric Society, vol. 75(6), pages 1721-1770, November.
  3. Costa-Gomes, Miguel & Crawford, Vincent P. & Broseta, Bruno, 1998. "Cognition and Behavior in Normal-Form Games: An Experimental Study," University of California at San Diego, Economics Working Paper Series qt1vn4h7x5, Department of Economics, UC San Diego.
  4. Kuhnen, Camelia & Knutson, Brian, 2008. "The Influence of Affect on Beliefs, Preferences and Financial Decisions," MPRA Paper 10410, University Library of Munich, Germany.
  5. Christopher Anderson, 2012. "Ambiguity aversion in multi-armed bandit problems," Theory and Decision, Springer, vol. 72(1), pages 15-33, January.
  6. Patrick Bajari & Ali Hortacsu, 2003. "Are Structural Estimates of Auction Models Reasonable? Evidence from Experimental Data," Working Papers 03002, Stanford University, Department of Economics.
  7. Rothschild, Michael, 1974. "A two-armed bandit theory of market pricing," Journal of Economic Theory, Elsevier, vol. 9(2), pages 185-202, October.
  8. Yingyao Hu & Matthew Shum, 2008. "Nonparametric identification of dynamic models with unobserved state variables," CeMMAP working papers CWP13/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  9. Brocas, Isabelle & Camerer, Colin & Carrillo, Juan D & Wang, Stephanie W., 2009. "Measuring attention and strategic behavior in games with private information," CEPR Discussion Papers 7529, C.E.P.R. Discussion Papers.
  10. James Choi & David Laibson & Brigitte Madrian & Andrew Metrick, 2007. "Reinforcement Learning and Savings Behavior," Yale School of Management Working Papers amz2657, Yale School of Management, revised 01 Mar 2009.
  11. Jeffrey Banks & David Porter & Mark Olson, 1997. "An experimental analysis of the bandit problem," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 10(1), pages 55-77.
  12. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, 07.
  13. Joseph Tao-yi Wang & Michael Spezio & Colin F. Camerer, 2010. "Pinocchio's Pupil: Using Eyetracking and Pupil Dilation to Understand Truth Telling and Deception in Sender-Receiver Games," American Economic Review, American Economic Association, vol. 100(3), pages 984-1007, June.
  14. Nathaniel T Wilcox, 2006. "Theories of Learning in Games and Heterogeneity Bias," Econometrica, Econometric Society, vol. 74(5), pages 1271-1292, 09.
  15. Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
  16. Avi Goldfarb & Mo Xiao, 2011. "Who Thinks about the Competition? Managerial Ability and Strategic Entry in US Local Telephone Markets," American Economic Review, American Economic Association, vol. 101(7), pages 3130-61, December.
  17. K. Carrie Armel & Antonio Rangel, 2008. "The Impact of Computation Time and Experience on Decision Values," American Economic Review, American Economic Association, vol. 98(2), pages 163-68, May.
  18. Elena Reutskaja & Rosemarie Nagel & Colin F. Camerer & Antonio Rangel, 2011. "Search Dynamics in Consumer Choice under Time Pressure: An Eye-Tracking Study," American Economic Review, American Economic Association, vol. 101(2), pages 900-926, April.
  19. 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.
  20. Yaw Nyarko & Andrew Schotter, 2002. "An Experimental Study of Belief Learning Using Elicited Beliefs," Econometrica, Econometric Society, vol. 70(3), pages 971-1005, May.
  21. Daniel A. Ackerberg, 2003. "Advertising, learning, and consumer choice in experience good markets: an empirical examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(3), pages 1007-1040, 08.
  22. Johnson, Eric J. & Camerer, Colin & Sen, Sankar & Rymon, Talia, 2002. "Detecting Failures of Backward Induction: Monitoring Information Search in Sequential Bargaining," Journal of Economic Theory, Elsevier, vol. 104(1), pages 16-47, May.
  23. Tat Y. Chan & Barton H. Hamilton, 2006. "Learning, Private Information, and the Economic Evaluation of Randomized Experiments," Journal of Political Economy, University of Chicago Press, vol. 114(6), pages 997-1040, December.
  24. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-54, May.
  25. Noah Gans & George Knox & Rachel Croson, 2007. "Simple Models of Discrete Choice and Their Performance in Bandit Experiments," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 383-408, December.
  26. Bergemann, Dirk & Hege, Ulrich, 1997. "Venture Capital Financing, Moral Hazard and Learning," CEPR Discussion Papers 1738, C.E.P.R. Discussion Papers.
  27. Charness, Gary & Levin, Dan, 2003. "When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect," University of California at Santa Barbara, Economics Working Paper Series qt7g63k28w, Department of Economics, UC Santa Barbara.
  28. Pakes, Ariel & McGuire, Paul, 2001. "Stochastic Algorithms, Symmetric Markov Perfect Equilibrium, and the 'Curse' of Dimensionality," Econometrica, Econometric Society, vol. 69(5), pages 1261-81, September.
  29. K. Carrie Armel & Aurelie Beaumel & Antonio Rangel, 2008. "Biasing simple choices by manipulating relative visual attention," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 3, pages 396-403, June.
  30. Hu, Yingyao, 2008. "Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution," Journal of Econometrics, Elsevier, vol. 144(1), pages 27-61, May.
  31. Andrew Caplin & Mark Dean & Paul W. Glimcher & Robb B. Rutledge, 2010. "Measuring Beliefs and Rewards: A Neuroeconomic Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 125(3), pages 923-960.
  32. Miller, Robert A, 1984. "Job Matching and Occupational Choice," Journal of Political Economy, University of Chicago Press, vol. 92(6), pages 1086-120, December.
  33. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
  34. Daniel T. Knoepfle & Joseph Tao-yi Wang & Colin F. Camerer, 2009. "Studying Learning in Games Using Eye-Tracking," Journal of the European Economic Association, MIT Press, vol. 7(2-3), pages 388-398, 04-05.
  35. Robert J. Meyer & Yong Shi, 1995. "Sequential Choice Under Ambiguity: Intuitive Solutions to the Armed-Bandit Problem," Management Science, INFORMS, vol. 41(5), pages 817-834, May.
  36. Jovanovic, Boyan, 1979. "Job Matching and the Theory of Turnover," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 972-90, October.
  37. Alexander L. Brown & Colin F. Camerer & Dan Lovallo, 2012. "To Review or Not to Review? Limited Strategic Thinking at the Movie Box Office," American Economic Journal: Microeconomics, American Economic Association, vol. 4(2), pages 1-26, May.
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