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Modeling the Change of Paradigm: Non-Bayesian Reactions to Unexpected News

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  • Pietro Ortoleva

Abstract

Bayes' rule has two well-known limitations: 1) it does not model the reaction to zero-probability events; 2) a sizable empirical evidence documents systematic violations of it. We characterize axiomatically an alternative updating rule, the Hypothesis Testing model. According to it, the agent follows Bayes' rule if she receives information to which she assigned a probability above a threshold. Otherwise, she looks at a prior over priors, updates it using Bayes' rule for second-order priors, and chooses the prior to which the updated prior over priors assigns the highest likelihood. We also present an application to equilibrium refinement in game theory. (JEL D11, D81, D83)

Suggested Citation

  • Pietro Ortoleva, 2012. "Modeling the Change of Paradigm: Non-Bayesian Reactions to Unexpected News," American Economic Review, American Economic Association, vol. 102(6), pages 2410-2436, October.
  • Handle: RePEc:aea:aecrev:v:102:y:2012:i:6:p:2410-36
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    Citations

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    Cited by:

    1. Minardi, Stefania & Savochkin, Andrei, 2019. "Subjective contingencies and limited Bayesian updating," Journal of Economic Theory, Elsevier, vol. 183(C), pages 1-45.
    2. Riella, Gil, 2013. "Preference for Flexibility and Dynamic Consistency," Journal of Economic Theory, Elsevier, vol. 148(6), pages 2467-2482.
    3. Bogaçhan Çelen & Sen Geng & Huihui Li, 2018. "Belief Error and Non-Bayesian Social Learning: An Experimental Evidence," GRU Working Paper Series GRU_2018_022, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    4. Joshua S. Gans & Peter Landry, 2019. "Self-recognition in teams," International Journal of Game Theory, Springer;Game Theory Society, vol. 48(4), pages 1169-1201, December.
    5. He, Xue Dong & Xiao, Di, 2017. "Processing consistency in non-Bayesian inference," Journal of Mathematical Economics, Elsevier, vol. 70(C), pages 90-104.
    6. Dipjyoti Majumdar & Artyom Shneyerov & Huan Xie, 2010. "How Optimism Leads to Price Discovery and Efficiency in a Dynamic Matching Market," Working Papers 10004, Concordia University, Department of Economics.
    7. Becker, Christoph K. & Melkonyan, Tigran & Proto, Eugenio & Sofianos, Andis & Trautmann, Stefan T., 2020. "Reverse Bayesianism: Revising Beliefs in Light of Unforeseen Events," IZA Discussion Papers 13821, Institute of Labor Economics (IZA).
    8. Suehyun Kwon, 2019. "Behavioral Players in a Game," CESifo Working Paper Series 7504, CESifo.
    9. Robert C. Merton & Richard T. Thakor, 2015. "Customers and Investors: A Framework for Understanding Financial Institutions," NBER Working Papers 21258, National Bureau of Economic Research, Inc.
    10. Aislinn Bohren & Daniel Hauser, 2018. "Social Learning with Model Misspeciification: A Framework and a Robustness Result," PIER Working Paper Archive 18-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Jul 2018.
    11. Nick Saponara, 2018. "Bayesian optimism," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 66(2), pages 375-406, August.
    12. Razin, Ronny & Levy, Gilat, 2020. "The drowning out of moderate voices: a maximum likelihood approach to combining forecasts," LSE Research Online Documents on Economics 104116, London School of Economics and Political Science, LSE Library.
    13. Heng Chen & Wing Suen, 2016. "Falling Dominoes: A Theory of Rare Events and Crisis Contagion," American Economic Journal: Microeconomics, American Economic Association, vol. 8(1), pages 228-255, February.
    14. Ellis, Andrew, 2018. "Foundations for optimal inattention," Journal of Economic Theory, Elsevier, vol. 173(C), pages 56-94.
    15. Sun, Lan, 2019. "Hypothesis testing equilibrium in signalling games," Mathematical Social Sciences, Elsevier, vol. 100(C), pages 29-34.
    16. Xiaoyu Cheng, 2019. "Relative Maximum Likelihood Updating of Ambiguous Beliefs," Papers 1911.02678, arXiv.org, revised Jun 2020.
    17. Edi Karni & Marie-Louise Vierø, 2015. "Probabilistic sophistication and reverse Bayesianism," Journal of Risk and Uncertainty, Springer, vol. 50(3), pages 189-208, June.
    18. Miguel Angel Ropero, 2019. "Pricing Policies in a Market With Asymmetric Information and Non-Bayesian Firms," Annals of Economics and Finance, Society for AEF, vol. 20(2), pages 541-563, November.
    19. Roberta De Filippis & Antonio Guarino & Philippe Jehiel & Toru Kitagawa, 2018. "Non-Bayesian updating in a social learning experiment," CeMMAP working papers CWP39/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Basu, Pathikrit, 2019. "Bayesian updating rules and AGM belief revision," Journal of Economic Theory, Elsevier, vol. 179(C), pages 455-475.
    21. Sun, Lan, 2016. "Hypothesis testing equilibrium in signaling games," Center for Mathematical Economics Working Papers 557, Center for Mathematical Economics, Bielefeld University.
    22. Sinkey, Michael, 2015. "How do experts update beliefs? Lessons from a non-market environment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 57(C), pages 55-63.

    More about this item

    JEL classification:

    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • 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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