Econ Papers Daily / 期刊论文
最优信息量排名与消费者搜索
Optimally informative rankings and consumer search
摘要
This paper investigates the optimal information policy of an online platform (or multi-product firm) when ranking products in response to a consumer search query. The informativeness of rankings ranges from full information to full obfuscation, and consumers learn their match values with the products by engaging in costly sequential search. Invoking continuous match value distributions allows us to establish a novel result about consumer search. While consumers buy products with high match values and continue searching when they encounter low match values, they abort search without buying a product for intermediate ones. For a large class of distributions, the optimal strategy of a platform maximizing the probability of the consumer buying a product is to provide either full information or the smallest amount of information subject to the constraint that the consumer starts searching. As a result, platform and consumer welfare are either fully aligned or at odds with each other.
中文摘要
本文探讨了在线平台（或多产品企业）在回应消费者搜索查询时对产品进行排序的最优信息政策。排序的信息含量范围从完全信息到完全混淆，消费者通过进行有成本的序贯搜索来了解其与产品的匹配值。利用连续匹配值分布，我们得以建立关于消费者搜索的新颖结论。消费者在遇到高匹配值时购买产品，在遇到低匹配值时继续搜索，而在遇到中等匹配值时则中止搜索且不购买任何产品。对于一大类分布而言，平台最大化消费者购买产品概率的最优策略是提供完全信息，或在消费者开始搜索的约束下提供最少量的信息。因此，平台与消费者福利要么完全一致，要么相互对立。