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Learning from a black box

Author

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  • Ke, Shaowei
  • Wu, Brian
  • Zhao, Chen

Abstract

We introduce a learning model in which the decision maker does not know how recommendations are generated, called the contraction rule. We present behavioral postulates that characterize it. The contraction rule can be uniquely identified and reveals how the decision maker interprets and how much she trusts the recommendation. In a dynamic stationary setting, we show that the contraction rule is not dominated by completely following recommendations and is incompatible with a property called compliance with balanced recommendations. Following this negative result, we demonstrate that the contraction rule may generate and reinforce recency bias and disagreement.

Suggested Citation

  • Ke, Shaowei & Wu, Brian & Zhao, Chen, 2024. "Learning from a black box," Journal of Economic Theory, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:jetheo:v:221:y:2024:i:c:s0022053124000929
    DOI: 10.1016/j.jet.2024.105886
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    References listed on IDEAS

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    1. Evan Sadler, 2021. "A Practical Guide to Updating Beliefs From Contradictory Evidence," Econometrica, Econometric Society, vol. 89(1), pages 415-436, January.
    2. Ke, Shaowei & Zhao, Chen, 2024. "From local utility to neural networks," Journal of Mathematical Economics, Elsevier, vol. 113(C).
    3. T Renee Bowen & Danil Dmitriev & Simone Galperti, 2023. "Learning from Shared News: When Abundant Information Leads to Belief Polarization," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(2), pages 955-1000.
    4. Simone Cerreia-Vioglio & Roberto Corrao & Giacomo Lanzani, 2024. "Dynamic Opinion Aggregation: Long-Run Stability and Disagreement," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(3), pages 1406-1447.
    5. Gajdos, T. & Hayashi, T. & Tallon, J.-M. & Vergnaud, J.-C., 2008. "Attitude toward imprecise information," Journal of Economic Theory, Elsevier, vol. 140(1), pages 27-65, May.
    6. Sandeep Baliga & Eran Hanany & Peter Klibanoff, 2013. "Polarization and Ambiguity," American Economic Review, American Economic Association, vol. 103(7), pages 3071-3083, December.
    7. Chambers, Christopher P. & Hayashi, Takashi, 2010. "Bayesian consistent belief selection," Journal of Economic Theory, Elsevier, vol. 145(1), pages 432-439, January.
    8. Benjamin Golub & Matthew O. Jackson, 2012. "How Homophily Affects the Speed of Learning and Best-Response Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1287-1338.
    9. Benjamin Golub & Matthew O. Jackson, 2010. "Naïve Learning in Social Networks and the Wisdom of Crowds," American Economic Journal: Microeconomics, American Economic Association, vol. 2(1), pages 112-149, February.
    10. Damiano Ettore, 2006. "Choice under Limited Uncertainty," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 6(1), pages 1-37, September.
    11. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
    12. Ravi Bansal & Ivan Shaliastovich, 2010. "Confidence Risk and Asset Prices," American Economic Review, American Economic Association, vol. 100(2), pages 537-541, May.
    13. Park, Hyoeun & Tayawa, Jason Paulo, 2024. "Anchored belief updating from recommendations," Journal of Mathematical Economics, Elsevier, vol. 110(C).
    14. Jacopo Perego & Sevgi Yuksel, 2022. "Media Competition and Social Disagreement," Econometrica, Econometric Society, vol. 90(1), pages 223-265, January.
    15. Larry G. Epstein, 2006. "An Axiomatic Model of Non-Bayesian Updating," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(2), pages 413-436.
    16. repec:ris:msuecw:2022_005 is not listed on IDEAS
    17. 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.
    18. Cesaltina Pacheco Pires, 2002. "A Rule For Updating Ambiguous Beliefs," Theory and Decision, Springer, vol. 53(2), pages 137-152, September.
    19. Pooya Molavi & Alireza Tahbaz‐Salehi & Ali Jadbabaie, 2018. "A Theory of Non‐Bayesian Social Learning," Econometrica, Econometric Society, vol. 86(2), pages 445-490, March.
    20. Cheng, Xiaoyu, 2022. "Relative Maximum Likelihood updating of ambiguous beliefs," Journal of Mathematical Economics, Elsevier, vol. 99(C).
    21. Matthew Kovach, 2021. "Conservative Updating," Papers 2102.00152, arXiv.org.
    22. Zhao, Chen, 2022. "Pseudo-Bayesian updating," Theoretical Economics, Econometric Society, vol. 17(1), January.
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