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