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Informational Asymmetries and Observational Learning in Search

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Abstract

As economics modeling moves from super rational decision makers to considering boundedly rational agents, some economic problems deserve a second look. This paper studies the effects of learning on the efficiency of search. Once learning is taken into account, the structure of information flow becomes important. In particular, I highlight the truncated information structure in the search problem. Agents stop searching once a sufficiently attractive price is found. Therefore, they observe the performance of shorter searches, but do not directly observe the performance of longer searches. I design and conduct an experiment to test the hypothesis that this asymmetric flow of information leads agents to search too little. I find strong evidence in its favor. This suggests that in the presence of learning, the provision of a more symmetric information structure will make search more efficient.

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Authors and Affiliations

  1. Department of Economics, Stanford University, Stanford, CA, 94305-6072, USA

    Liran Einav

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  1. Liran Einav
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Correspondence to Liran Einav.

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JEL Classification: C91, D83

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Einav, L. Informational Asymmetries and Observational Learning in Search. J Risk Uncertainty 30, 241–259 (2005). https://doi.org/10.1007/s11166-005-6563-7

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  • DOI: https://doi.org/10.1007/s11166-005-6563-7

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