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Optimal Feedback in Contests
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AI
DOI：10.1093/restud/rdac074.png)
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Abstract
En 中文
We obtain optimal dynamic contests for environments where the designer monitors effort through coarse, binary signals-Poisson successes-and aims to elicit maximum effort, ideally in the least amount of time possible, given a fixed prize. The designer has a vast set of contests to choose from, featuring termination and prize-allocation rules together with real-time feedback for the contestants. Every effort-maximizing contest (which also maximizes total expected successes) has a history-dependent termination rule, a feedback policy that keeps agents fully apprised of their own success, and a prize-allocation rule that grants them, in expectation, a time-invariant share of the prize if they succeed. Any contest that achieves this effort in the shortest possible time must in addition be what we call second chance: once a pre-specified number of successes arrive, the contest enters a countdown phase where contestants are given one last chance to succeed.
Keywords:
Contests
Moral hazard
Agency problem
Incentives
Information design
D2
D8
M5
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