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