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Response functions

Author

Listed:
  • Oyarzun, Carlos
  • Sanjurjo, Adam
  • Nguyen, Hien

Abstract

Imagine that John must choose between two uncertain payoff distributions, knowing that the set of possible payoffs is the same for both, but nothing about the shapes of the distributions. In the first period he chooses either alternative and experiences a payoff as a result of his choice. Given this experienced payoff, in the second period he decides whether to choose the same alternative again, or switch. We model John’s second period choice with a response function, i.e., a mapping from obtained payoffs to the probability of choosing the same alternative in the second period. We first provide results on (i) how the shape of the response function affects both expected payoffs and exposure to risk, and (ii) what standard models of choice under uncertainty would predict about the shape of the response function. We then run an experiment to elicit subjects’ response functions, empirically characterize the heterogeneity across subjects with a mixture model, and illustrate how payoffs vary across response function types. Finally, we use our theoretical results, along with additional information that we collected from subjects, to interpret their response functions.

Suggested Citation

  • Oyarzun, Carlos & Sanjurjo, Adam & Nguyen, Hien, 2017. "Response functions," European Economic Review, Elsevier, vol. 98(C), pages 1-31.
  • Handle: RePEc:eee:eecrev:v:98:y:2017:i:c:p:1-31
    DOI: 10.1016/j.euroecorev.2017.06.011
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    References listed on IDEAS

    as
    1. Agastya, Murali & Slinko, Arkadii, 2015. "Dynamic choice in a complex world," Journal of Economic Theory, Elsevier, vol. 158(PA), pages 232-258.
    2. 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.
    3. Joshua B. Miller & Adam Sanjurjo, 2015. "Surprised by the Gambler’s and Hot Hand Fallacies? A Truth in the Law of Small Numbers," Working Papers 552, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    4. Shlomo Benartzi, 2001. "Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock," Journal of Finance, American Finance Association, vol. 56(5), pages 1747-1764, October.
    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 848-881, September.
    6. 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.
    7. 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.
    8. Ed Hopkins, 2002. "Two Competing Models of How People Learn in Games," Econometrica, Econometric Society, vol. 70(6), pages 2141-2166, November.
    9. Tilman Börgers & Antonio J. Morales & Rajiv Sarin, 2004. "Expedient and Monotone Learning Rules," Econometrica, Econometric Society, vol. 72(2), pages 383-405, March.
    10. Fay, Michael P. & Shaw, Pamela A., 2010. "Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i02).
    11. Holt, Charles A. & Smith, Angela M., 2009. "An update on Bayesian updating," Journal of Economic Behavior & Organization, Elsevier, vol. 69(2), pages 125-134, February.
    12. Markku Kaustia & Samuli Knüpfer, 2008. "Do Investors Overweight Personal Experience? Evidence from IPO Subscriptions," Journal of Finance, American Finance Association, vol. 63(6), pages 2679-2702, December.
    13. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.
    14. 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.
    15. Drazen Prelec, 1998. "The Probability Weighting Function," Econometrica, Econometric Society, vol. 66(3), pages 497-528, May.
    16. William Neilson, 2010. "A simplified axiomatic approach to ambiguity aversion," Journal of Risk and Uncertainty, Springer, vol. 41(2), pages 113-124, October.
    17. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters,in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98 World Scientific Publishing Co. Pte. Ltd..
    18. Christopher Anderson, 2012. "Ambiguity aversion in multi-armed bandit problems," Theory and Decision, Springer, vol. 72(1), pages 15-33, January.
    19. 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.
    20. Mengel Friederike & Rivas Javier, 2012. "An Axiomatization of Learning Rules when Counterfactuals are not Observed," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 12(1), pages 1-19, July.
    21. Wilcox, Nathaniel T., 2011. "'Stochastically more risk averse:' A contextual theory of stochastic discrete choice under risk," Journal of Econometrics, Elsevier, vol. 162(1), pages 89-104, May.
    22. Loomes, Graham & Sugden, Robert, 1982. "Regret Theory: An Alternative Theory of Rational Choice under Uncertainty," Economic Journal, Royal Economic Society, vol. 92(368), pages 805-824, December.
    23. Loomes, Graham & Sugden, Robert, 1995. "Incorporating a stochastic element into decision theories," European Economic Review, Elsevier, vol. 39(3-4), pages 641-648, April.
    24. Hu, Yingyao & Kayaba, Yutaka & Shum, Matthew, 2013. "Nonparametric learning rules from bandit experiments: The eyes have it!," Games and Economic Behavior, Elsevier, vol. 81(C), pages 215-231.
    25. Rothschild, Michael & Stiglitz, Joseph E., 1970. "Increasing risk: I. A definition," Journal of Economic Theory, Elsevier, vol. 2(3), pages 225-243, September.
    26. Zacharias Maniadis & Joshua Miller, 2012. "The Weight of Personal Experience: an Experimental Measurement," Working Papers 452, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    27. David M. Grether, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, Oxford University Press, vol. 95(3), pages 537-557.
    28. Friedman, Daniel, 1998. "Monty Hall's Three Doors: Construction and Deconstruction of a Choice Anomaly," American Economic Review, American Economic Association, vol. 88(4), pages 933-946, September.
    29. Oyarzun, Carlos & Sarin, Rajiv, 2012. "Mean and variance responsive learning," Games and Economic Behavior, Elsevier, vol. 75(2), pages 855-866.
    30. David Easley & Aldo Rustichini, 1999. "Choice without Beliefs," Econometrica, Econometric Society, vol. 67(5), pages 1157-1184, September.
    31. Grün, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
    32. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    33. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
    34. Simonsohn, Uri & Karlsson, Niklas & Loewenstein, George & Ariely, Dan, 2008. "The tree of experience in the forest of information: Overweighing experienced relative to observed information," Games and Economic Behavior, Elsevier, vol. 62(1), pages 263-286, January.
    35. Brit Grosskopf & Ido Erev & Eldad Yechiam, 2006. "Foregone with the Wind: Indirect Payoff Information and its Implications for Choice," International Journal of Game Theory, Springer;Game Theory Society, vol. 34(2), pages 285-302, August.
    36. 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.
    37. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
    38. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
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    More about this item

    Keywords

    Adaptive learning; Response functions; Experimental economics; Stochastic dominance;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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