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Decision Makers as Statisticians: Diversity, Ambiguity, and Learning

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  • Nabil I. Al-Najjar

Abstract

I study individuals who use frequentist models to draw uniform inferences from independent and identically distributed data. The main contribution of this paper is to show that distinct models may be consistent with empirical evidence, even in the limit when data increases without bound. Decision makers may then hold different beliefs and interpret their environment differently even though they know each other's model and base their inferences on the same evidence. The behavior modeled here is that of rational individuals confronting an environment in which learning is hard, rather than individuals beset by cognitive limitations or behavioral biases. Copyright 2009 The Econometric Society.

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  • Nabil I. Al-Najjar, 2009. "Decision Makers as Statisticians: Diversity, Ambiguity, and Learning," Econometrica, Econometric Society, vol. 77(5), pages 1371-1401, September.
    • Handle: RePEc:ecm:emetrp:v:77:y:2009:i:5:p:1371-1401
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    Citations

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    Cited by:

    1. Stinchcombe, Maxwell B., 2016. "Objective and subjective foundations for multiple priors," Journal of Economic Theory, Elsevier, vol. 165(C), pages 263-291.
    2. Christoph March, 2011. "Adaptive social learning," PSE Working Papers halshs-00572528, HAL.
    3. Esponda, Ignacio & Pouzo, Demian & Yamamoto, Yuichi, 2021. "Asymptotic behavior of Bayesian learners with misspecified models," Journal of Economic Theory, Elsevier, vol. 195(C).
    4. George J. Mailath & Larry Samuelson, 2020. "Learning under Diverse World Views: Model-Based Inference," American Economic Review, American Economic Association, vol. 110(5), pages 1464-1501, May.
    5. Martin, Ian W.R. & Nagel, Stefan, 2022. "Market efficiency in the age of big data," Journal of Financial Economics, Elsevier, vol. 145(1), pages 154-177.
    6. Luciano Castro & Alain Chateauneuf, 2011. "Ambiguity aversion and trade," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 48(2), pages 243-273, October.
    7. Jong Jae Lee, 2018. "Formalization of information: knowledge and belief," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 66(4), pages 1007-1022, December.
    8. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    9. Jose Luis Montiel Olea & Pietro Ortoleva & Mallesh M Pai & Andrea Prat, 2019. "Competing Models," Papers 1907.03809, arXiv.org, revised Nov 2021.
    10. Ignacio Esponda & Demian Pouzo, 2014. "Berk-Nash Equilibrium: A Framework for Modeling Agents with Misspecified Models," Papers 1411.1152, arXiv.org, revised Nov 2019.
    11. Ignacio Esponda & Demian Pouzo, 2015. "Equilibrium in Misspecified Markov Decision Processes," Papers 1502.06901, arXiv.org, revised May 2016.
    12. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    13. Ignacio Esponda & Demian Pouzo & Yuichi Yamamoto, 2019. "Asymptotic Behavior of Bayesian Learners with Misspecified Models," Papers 1904.08551, arXiv.org, revised Oct 2019.
    14. Pai, Mallesh & Hansen, Karsten, 2020. "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers 14372, C.E.P.R. Discussion Papers.
    15. Al-Najjar, Nabil I. & Pai, Mallesh M., 2014. "Coarse decision making and overfitting," Journal of Economic Theory, Elsevier, vol. 150(C), pages 467-486.
    16. Tao Li, 2013. "Investors' Heterogeneity and Implied Volatility Smiles," Management Science, INFORMS, vol. 59(10), pages 2392-2412, October.
    17. In-Koo Cho & Jonathan Libgober, 2021. "Machine Learning for Strategic Inference," Papers 2101.09613, arXiv.org.
    18. Antonella Tutino, 2015. "Information Transmission and Rational Inattention," 2015 Meeting Papers 286, Society for Economic Dynamics.

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