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The Model Selection Curse

Eliaz, K; Spiegler, R; (2019) The Model Selection Curse. American Economic Review: Insights , 1 (2) pp. 127-140. 10.1257/aeri.20180485. Green open access

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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.

Type: Article
Title: The Model Selection Curse
Open access status: An open access version is available from UCL Discovery
DOI: 10.1257/aeri.20180485
Publisher version: https://doi.org/10.1257/aeri.20180485
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10062355
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