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The Model Selection Curse

delete2019-09-01
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PRE
AI
K
Kfir Eliaz *
R
Ran Spiegler
DOI:10.1257/aeri.20180485delete
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摘要

摘要

En 中文
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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期刊

American Economic Review 封面图
American Economic Review
IF:
11.6
论文数:
5.0K
被引数:
7.5W

机构

T
Tel Aviv University
学者数:
3.7W
论文数: 3.0W
被引数: 3.6W
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