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摘要翻译：
“统计学家”代表代理人采取行动，基于代理人自我报告的个人数据和涉及其他人的样本。他采取的行动是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：在给定统计学家的程序的情况下，对代理人来说，说实话是最优的吗？我们通过一个简单的例子来分析这个问题，这个例子强调了模型选择的作用。我们认为，我们的简单练习可能会对人类与“机器学习”算法交互的更广泛问题产生影响。
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英文标题：
《The Model Selection Curse》
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作者：
Kfir Eliaz and Ran Spiegler
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Theoretical Economics 理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/1810.02888
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