  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.

Theoretical Economics