标题： 模型选择的困境 显示英文标题

标题： The Model Selection Curse

摘要： 一个“统计学家”根据代理人的自我报告的个人数据和其他人的样本，代表代理人的利益采取行动。 他采取的行动是基于代理人报告的估计函数。 估计过程涉及模型选择。 我们提出以下问题：在统计学家的程序下，对代理人来说，如实报告是否是最优的？ 我们在一个简单的例子中分析这个问题，该例子突出了模型选择的作用。 我们认为，我们的简单练习可能对人类与“机器学习”算法的更广泛互动问题具有启示意义。 显示英文摘要

摘要： 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.