  Data-based decisionmaking 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
becomes less informative when decisions depend more strongly on data. We
formalize why and how a decisionmaker should commit to underutilizing data.
Doing so attenuates information loss and thereby improves allocation accuracy.
