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An Experiment On Social Mislearning

Author

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  • Eyster, Erik

    (London School of Economics and Political Science)

  • Rabin, Matthew

    (Harvard University)

  • Weizsäcker, Georg

    (HU Berlin)

Abstract

We investigate experimentally whether social learners appreciate the redundancy of information conveyed by their observed predecessors\' actions. Each participant observes a private signal and enters an estimate of the sum of all earlier-moving participants\' signals plus her own. In a first treatment, participants move single-file and observe all predecessors\' entries; Bayesian Nash Equilibrium (BNE) predicts that each participant simply add her signal to her immediate predecessor\'s entry. Although 75% of participants do so, redundancy neglect by the other 25% generates excess imitation and mild inefficiencies. In a second treatment, participants move four per period; BNE predicts that most players anti-imitate some observed entries. Such anti-imitation occurs in 35% of the most transparent cases, and 16% overall. The remaining redundancy neglect creates dramatic excess imitation and inefficiencies: late-period entries are far too extreme, and on average participants would earn substantially more by ignoring their predecessors altogether.

Suggested Citation

  • Eyster, Erik & Rabin, Matthew & Weizsäcker, Georg, 2018. "An Experiment On Social Mislearning," Rationality and Competition Discussion Paper Series 73, CRC TRR 190 Rationality and Competition.
  • Handle: RePEc:rco:dpaper:73
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    Cited by:

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    2. Marta Serra-Garcia & Uri Gneezy, 2021. "Mistakes, Overconfidence, and the Effect of Sharing on Detecting Lies," American Economic Review, American Economic Association, vol. 111(10), pages 3160-3183, October.
    3. Cheng, Ing-Haw & Hsiaw, Alice, 2022. "Distrust in experts and the origins of disagreement," Journal of Economic Theory, Elsevier, vol. 200(C).
    4. Theo Offerman & Giorgia Romagnoli & Andreas Ziegler, 2022. "Why are open ascending auctions popular? The role of information aggregation and behavioral biases," Quantitative Economics, Econometric Society, vol. 13(2), pages 787-823, May.
    5. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    6. March, Christoph & Ziegelmeyer, Anthony, 2020. "Altruistic observational learning," Journal of Economic Theory, Elsevier, vol. 190(C).
    7. Nicolas Astier, 2018. "Comparative Feedbacks under Incomplete Information," Post-Print hal-01465189, HAL.
    8. Astier, Nicolas, 2018. "Comparative feedbacks under incomplete information," Resource and Energy Economics, Elsevier, vol. 54(C), pages 90-108.
    9. Duffy, John & Hopkins, Ed & Kornienko, Tatiana, 2021. "Lone wolf or herd animal? Information choice and learning from others," European Economic Review, Elsevier, vol. 134(C).
    10. Dasaratha, Krishna & He, Kevin, 2021. "An experiment on network density and sequential learning," Games and Economic Behavior, Elsevier, vol. 128(C), pages 182-192.
    11. Krishna Dasaratha & Kevin He, 2019. "Aggregative Efficiency of Bayesian Learning in Networks," Papers 1911.10116, arXiv.org, revised Jan 2023.
    12. Sadler, Evan, 2020. "Innovation adoption and collective experimentation," Games and Economic Behavior, Elsevier, vol. 120(C), pages 121-131.
    13. Nicolas Astier, 2016. "Comparative Feedbacks under Incomplete Information," Working Papers hal-01465189, HAL.
    14. Duffy, John & Hopkins, Ed & Kornienko, Tatiana & Ma, Mingye, 2019. "Information choice in a social learning experiment," Games and Economic Behavior, Elsevier, vol. 118(C), pages 295-315.

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    More about this item

    Keywords

    social learning; redundancy neglect; experiments; higher-order beliefs;
    All these keywords.

    JEL classification:

    • B49 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Other

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