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Bayesian Networks and Boundedly Rational Expectations

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  • Spiegler, Ran

Abstract

I present a framework for analyzing decision makers with an imperfect understanding of their environment's correlation structure. The decision maker faces an objective multivariate probability distribution (his own action is one of the random variables). He is characterized by a directed acyclic graph over the set of variables. His subjective belief filters the objective distribution through his graph, via the factorization formula for Bayesian networks. This belief distortion implies that the decision maker's long-run behavior may affect his perception of the consequences of his actions. Accordingly, I define a "personal equilibrium" notion of optimal choices. I show how recent models of boundedly rational expectations (as well as new ones, e.g. reverse causality) can be subsumed into this framework as special cases. Some general properties of the Bayesian-network representation of subjective beliefs are presented, as well as a "missing data" foundation.

Suggested Citation

  • Spiegler, Ran, 2014. "Bayesian Networks and Boundedly Rational Expectations," CEPR Discussion Papers 10062, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:10062
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    Cited by:

    1. Levy, Gilat & Razin, Ronny, 2021. "A maximum likelihood approach to combining forecasts," LSE Research Online Documents on Economics 104116, London School of Economics and Political Science, LSE Library.
    2. Magne Mogstad & Emmanuel Dhyne & Ayumu Kikkawa & Felix Tintelnot, 2017. "Trade and Domestic Production Networks," 2017 Meeting Papers 381, Society for Economic Dynamics.
    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. Jehiel, Philippe & Mohlin, Erik, 2021. "Cycling and Categorical Learning in Decentralized Adverse Selection Economies," Working Papers 2021:11, Lund University, Department of Economics.
    5. 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.
    6. Kfir Eliaz & Ran Spiegler, 2020. "A Model of Competing Narratives," American Economic Review, American Economic Association, vol. 110(12), pages 3786-3816, December.
    7. Pleshcheva, Vlada & Klapper, Daniel & Dannewald, Till, 2019. "On Factors of Consumer Heterogeneity in (Mis)Valuation of Future Energy Costs: Evidence for the German Automobile Market," Rationality and Competition Discussion Paper Series 140, CRC TRR 190 Rationality and Competition.
    8. Hagenbach, Jeanne & Koessler, Frédéric, 2020. "Cheap talk with coarse understanding," Games and Economic Behavior, Elsevier, vol. 124(C), pages 105-121.
    9. Yves Le Yaouanq & Peter Schwardmann, 2019. "Learning about One's Self," CESifo Working Paper Series 7455, CESifo.
    10. Schneider, Eric B., 2020. "Collider bias in economic history research," Explorations in Economic History, Elsevier, vol. 78(C).
    11. Gopal K. Basak & Arnab Bhattacharjee & Samarjit Das, 2018. "Causal ordering and inference on acyclic networks," Empirical Economics, Springer, vol. 55(1), pages 213-232, August.
    12. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    13. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Misinterpreting Others and the Fragility of Social Learning," Econometrica, Econometric Society, vol. 88(6), pages 2281-2328, November.
    14. Ignacio Esponda & Demian Pouzo, 2017. "Conditional Retrospective Voting in Large Elections," American Economic Journal: Microeconomics, American Economic Association, vol. 9(2), pages 54-75, May.
    15. Spiegler, Ran, 2018. "News and Archival Information in Games," CEPR Discussion Papers 12805, C.E.P.R. Discussion Papers.
    16. Ignacio Esponda & Demian Pouzo, 2014. "Berk-Nash Equilibrium: A Framework for Modeling Agents with Misspecified Models," Papers 1411.1152, arXiv.org, revised Nov 2019.
    17. Ignacio Esponda & Demian Pouzo & Yuichi Yamamoto, 2019. "Asymptotic Behavior of Bayesian Learners with Misspecified Models," Papers 1904.08551, arXiv.org, revised Oct 2019.
    18. Pablo Schenone, 2018. "Causality: a decision theoretic approach," Papers 1812.07414, arXiv.org, revised Apr 2020.
    19. Andrew Ellis & Heidi Christina Thysen, 2021. "Subjective Causality in Choice," Papers 2106.05957, arXiv.org, revised Oct 2021.

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    More about this item

    Keywords

    Bayesian networks; boundedly rational expectations; coarse reasoning; directed acyclic graphs; misspecified models; personal equilibrium; reverse causality;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

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