Improving Information from Manipulable Data

Abstract Data-based decision making must account for the manipulation of data by agents who are aware of how decisions are being made and want to affect their allocations. We study a framework in which, due to such manipulation, data become less informative when decisions depend more strongly on data. We formalize why and how a decision maker should commit to underutilizing data. Doing so attenuates information loss and thereby improves allocation accuracy.