The Model Selection Curse

Kfir Eliaz and Ran Spiegler ()

Papers from arXiv.org

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.

Date: 2018-10
New Economics Papers: this item is included in nep-big and nep-cmp
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Journal Article: The Model Selection Curse (2019)
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