Dynamic Amnesty Programs
研究监管者如何设计随时间变化的赦免计划，以激励罪犯自首，发现最优计划呈现周期性，高回报犯罪者在周期末自首，低回报者始终自首。
A regulator faces a stream of agents engaged in crimes with stochastic returns. The regulator designs an amnesty program, committing to a time path of punishments for criminals who report their crimes. In an optimal program, time variation in the returns from crime can generate time variation in the generosity of amnesty. I construct an optimal time path and show that it exhibits amnesty cycles. Amnesty becomes increasingly generous over time until it hits a bound, after which the cycle resets. Agents engaged in high return crime report at the end of each cycle, while agents engaged in low return crime report always.