Social Learning and Strategic Pricing with Rating Systems
研究了在线交易中评级系统将买家多样化意见简化为汇总统计后，卖家如何通过秘密降价操纵评级，导致间歇性闪购，并分析了这种操纵如何适得其反，以及评级系统对剩余分配的影响。
Rating systems, widely used in online transactions, often reduce buyers' diverse opinions to summary statistics. To explore the consequences of this coarse aggregation, we analyze a dynamic adverse selection model where buyers share anonymous evaluations via a rating system. With heterogeneous buyers, the seller is tempted to secretly lower prices to attract favorable ratings from price-sensitive buyers. That leads to sporadic flash sales. The seller's incentive to manipulate ratings is, however, self-defeating. Our analysis illustrates how the rating system shapes the allocation of surplus and offers insights for platform and product design.