When Samples Are Strategically Selected

In standard classification problems, the assumption is that the entity making the decision (the principal ) has access to all the samples. However, in many contexts, she either does not have direct access to the samples, or can inspect only a limited set of samples and does not know which are the most relevant ones. In such cases, she must rely on another party (the agent ) to either provide the samples or point out the most relevant ones. If the agent has a different objective, then the principal cannot trust the submitted samples to be representative. She must set a policy for how she makes decisions, keeping in mind the agent’s incentives. In this paper, we introduce a theoretical framework for this problem and provide key structural and computational results.

Authors

• Hanrui Zhang 0001
• Yu Cheng 0002
• Vincent Conitzer

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