Community Enforcement of Trust with Bounded Memory
研究在随机匹配的大社会中，当违约记录仅保留有限时间时，如何通过暂时排斥来维持信任，并发现粗信息结构通过内生逆向选择维持惩罚，而信任违约者能提高效率。
We examine how trust is sustained in large societies with random matching, when records of past transgressions are retained for a finite length of time. To incentivize trustworthiness, defaulters should be punished by temporary exclusion. However, it is profitable to trust defaulters who are on the verge of rehabilitation. With perfect bounded information, defaulter exclusion unravels and trust cannot be sustained, in any purifiable equilibrium. A coarse information structure, that pools recent defaulters with those nearing rehabilitation, endogenously generates adverse selection, sustaining punishments. Equilibria where defaulters are trusted with positive probability improve efficiency, by raising the proportion of likely re-offenders in the pool of defaulters.