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arXiv · 1810.02888

The Model Selection Curse

Kfir Eliaz·Ran Spiegler
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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.

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econ.TH

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Kfir Eliaz, Ran Spiegler. 2018-10-05. The Model Selection Curse. https://arxiv.org/abs/1810.02888

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