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Informational Herding with Model Misspecification, Second Version


  • Aislinn Bohren

    () (Department of Economics, University of Pennsylvania)


This paper demonstrates that a misspecified model of information processing interferes with long-run learning and allows inefficient choices to persist in the face of contradictory public information. I consider an observational learning environment where agents observe a private signal about a hidden state, and some agents observe the actions of their predecessors. Prior actions aggregate multiple sources of correlated information about the state, and agents face an inferential challenge to distinguish between new and redundant information. When individuals significantly overestimate the amount of new information, beliefs about the state become entrenched and incorrect learning may occur. When individuals sufficiently overestimate the amount of redundant information, beliefs are fragile and learning is incomplete. Learning is complete when agents have an approximately correct model of inference, establishing that the correct model is robust to perturbation. These results have important implications for timing, frequency and strength of policy interventions to facilitate learning.

Suggested Citation

  • Aislinn Bohren, 2014. "Informational Herding with Model Misspecification, Second Version," PIER Working Paper Archive 15-022, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Nov 2014.
  • Handle: RePEc:pen:papers:15-022

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    References listed on IDEAS

    1. Dorothea Kübler & Georg Weizsäcker, 2004. "Limited Depth of Reasoning and Failure of Cascade Formation in the Laboratory," Review of Economic Studies, Oxford University Press, vol. 71(2), pages 425-441.
    2. Anthony Ziegelmeyer & Frédéric Koessler & Juergen Bracht & Eyal Winter, 2010. "Fragility of information cascades: an experimental study using elicited beliefs," Experimental Economics, Springer;Economic Science Association, vol. 13(2), pages 121-145, June.
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