Optimal Windows for Aggregating Ratings in Electronic Marketplaces
研究了在线市场中广泛使用的窗口聚合机制，提出选择窗口大小的方法，以最大化卖家诚实行为的参数范围，并发现更多历史交易信息对耐心卖家更诚实但高质量卖家更不诚实。
Aseller in an online marketplace with an effective reputation mechanism should expect that dishonest behavior results in higher payments now whereas honest behavior results in a better reputation—and thus higher payments—in the future. We study the Window Aggregation Mechanism, a widely used class of mechanisms that shows the average value of the seller's ratings within some fixed window of past transactions. We suggest approaches for choosing the window size that maximizes the range of parameters for which it is optimal for the seller to be truthful. We show that mechanisms that use information from a larger number of past transactions tend to provide incentives for patient sellers to be more truthful but for higher-quality sellers to be less truthful.