Memory and Markets
研究了在动态市场中删除过往记录（如信用记录）对交易的影响，发现当卖家平均质量低时，无限记录会导致市场崩溃，而适当删除记录（负面记录保留更久、正面记录尽早删除）能维持交易并最大化社会福利。
Abstract In many environments, including credit and online markets, records about participants are collected, published, and erased after some time. We study the effects of erasing past records in a dynamic market where the quality of sellers follows a Markov process, and buyers leave feedback about sellers to an information intermediary. When the average quality of sellers is low, unlimited records lead to a market breakdown in the long run. We consider the information design problem and characterize information policies that can sustain trade and that maximize social welfare. These policies hide some information from the market in order to foster socially desirable experimentation. We show that these outcomes can be implemented by appropriately deleting past records. Crucially, positive and negative records play opposite roles with different intensities and must have different lengths: negative records must be deleted sufficiently late, and positive ones sufficiently early.