The Model Selection Curse
Theoretical Economics
2018-10-09 v1
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.
Cite
@article{arxiv.1810.02888,
title = {The Model Selection Curse},
author = {Kfir Eliaz and Ran Spiegler},
journal= {arXiv preprint arXiv:1810.02888},
year = {2018}
}