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The Model Selection Curse

Kfir Eliaz and Ran Spiegler ()

American Economic Review: Insights, 2019, vol. 1, issue 2, 127-40

Abstract: A statistician takes an action on behalf of an agent, based on the agent's self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent's report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician's procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with machine learning algorithms.

JEL-codes: C52 (search for similar items in EconPapers)
Date: 2019
Note: DOI: 10.1257/aeri.20180485
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