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Random Choice and Learning

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  • Paulo Natenzon

Abstract

Context-dependent individual choice challenges the principle of utility maximization. I explain context dependence as the optimal response of an imperfectly informed agent to the ease of comparison of the options. I introduce a discrete choice model, the Bayesian probit, which allows the analyst to identify stable preferences from context-dependent choice data. My model accommodates observed behavioral phenomena--including the attraction and compromise effects--that lie beyond the scope of any random utility model. I use data from frog mating choices to illustrate how the model can outperform the random utility framework in goodness of fit and out-of-sample prediction.

Suggested Citation

  • Paulo Natenzon, 2019. "Random Choice and Learning," Journal of Political Economy, University of Chicago Press, vol. 127(1), pages 419-457.
  • Handle: RePEc:ucp:jpolec:doi:10.1086/700762
    DOI: 10.1086/700762
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    Citations

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    Cited by:

    1. Duffy, Sean & Smith, John, 2020. "An economist and a psychologist form a line: What can imperfect perception of length tell us about stochastic choice?," MPRA Paper 99417, University Library of Munich, Germany.
    2. Andrew Caplin & Daniel Martin, 2015. "A Testable Theory of Imperfect Perception," Economic Journal, Royal Economic Society, vol. 125(582), pages 184-202, February.
    3. Ronayne, David & Brown, Gordon D.A., 2016. "Multi-Attribute Decision By Sampling : An Account Of The Attraction, Compromise And Similarity Effects," Economic Research Papers 269322, University of Warwick - Department of Economics.
    4. Ryan Webb, 2019. "The (Neural) Dynamics of Stochastic Choice," Management Science, INFORMS, vol. 65(1), pages 230-255, January.
    5. Filip Matêjka & Alisdair McKay, 2015. "Rational Inattention to Discrete Choices: A New Foundation for the Multinomial Logit Model," American Economic Review, American Economic Association, vol. 105(1), pages 272-298, January.
    6. Miguel Costa-Gomes & Georgios Gerasimou, 2020. "Status Quo Bias and the Decoy Effect: A Comparative Analysis in Choice under Risk," Papers 2006.14868, arXiv.org, revised Oct 2020.
    7. William Morrison & Dmitry Taubinsky, 2019. "Rules of Thumb and Attention Elasticities: Evidence from Under- and Overreaction to Taxes," NBER Working Papers 26180, National Bureau of Economic Research, Inc.
    8. Bertoli, Simone & Moraga, Jesús Fernández-Huertas & Guichard, Lucas, 2020. "Rational inattention and migration decisions," Journal of International Economics, Elsevier, vol. 126(C).
    9. Dutta, Rohan, 0. "Gradual pairwise comparison and stochastic choice," Theoretical Economics, Econometric Society.
    10. Victor H. Aguiar & Maria Jose Boccardi & Nail Kashaev & Jeongbin Kim, 2018. "Does Random Consideration Explain Behavior when Choice is Hard? Evidence from a Large-scale Experiment," Papers 1812.09619, arXiv.org, revised Jun 2019.
    11. Sean Horan & Paola Manzini & Marco Mariotti, 2018. "Precision May Harm: The Comparative Statics of Imprecise Judgement," Working Paper Series 1518, Department of Economics, University of Sussex Business School.
    12. Roy Allen & Pawel Dziewulski & John Rehbeck, 2019. "Revealed statistical consumer theory," Working Paper Series 1119, Department of Economics, University of Sussex Business School.
    13. Jose Apesteguia & Miguel Ángel Ballester, 2018. "Separating Predicted Randomness from Noise," Working Papers 1018, Barcelona Graduate School of Economics.
    14. Dewan, Ambuj & Neligh, Nathaniel, 2020. "Estimating information cost functions in models of rational inattention," Journal of Economic Theory, Elsevier, vol. 187(C).
    15. Duffy, Sean & Gussman, Steven & Smith, John, 2019. "Judgments of length in the economics laboratory: Are there brains in choice?," MPRA Paper 93126, University Library of Munich, Germany.
    16. S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    17. Lang, Ruitian, 2019. "Try before you buy: A theory of dynamic information acquisition," Journal of Economic Theory, Elsevier, vol. 183(C), pages 1057-1093.
    18. Gerasimou, Georgios, 2012. "Asymmetric Dominance, Deferral and Status Quo Bias in a Theory of Choice with Incomplete Preferences," MPRA Paper 40097, University Library of Munich, Germany.
    19. Georgios Gerasimou, 2020. "The Decision-Conflict and Multicriteria Logit," Papers 2008.04229, arXiv.org, revised Nov 2020.
    20. D. Pennesi, 2016. "Deciding fast and slow," Working Papers wp1082, Dipartimento Scienze Economiche, Universita' di Bologna.
    21. Michael Woodford, 2014. "An Optimizing Neuroeconomic Model of Discrete Choice," NBER Working Papers 19897, National Bureau of Economic Research, Inc.
    22. Michael Woodford, 2019. "Modeling Imprecision in Perception, Valuation and Choice," NBER Working Papers 26258, National Bureau of Economic Research, Inc.
    23. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci, 2020. "Multinomial logit processes and preference discovery: outside and inside the black box," Working Papers 663, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

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