The Model Selection Curse
- (pp. 127-40)
AbstractA 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.
CitationEliaz, Kfir, and Ran Spiegler. 2019. "The Model Selection Curse." American Economic Review: Insights, 1 (2): 127-40. DOI: 10.1257/aeri.20180485
- C52 Model Evaluation, Validation, and Selection