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5月11日,俞浩君(上海财经大学)
发布时间:2017-05-05   发布人:zs   点击数:1358

报告人:俞浩君,上海财经大学

时间:5月11日,周四下午1:30—3:00

地点:博学楼1222教室

题目:A Sequential Search Model with Partial Depth Evaluation

摘要:Conventional sequential search models typically assume that consumers either evaluate all relevant information of a product or do not evaluate any information. This paper provides a sequential search model in which consumers can optimally choose how much information to evaluate by selecting an evaluation depth (e.g., how many attributes to evaluate) for each selected product. We prove that, when there are infinite number of products, there is a stationary stopping rule, and the optimal evaluation depth is also stationary. Specifically, consumers optimally choose to evaluate products at a partial (full) depth when search cost is sufficiently high (low). Due to partial depth evaluation, firms' prices and consumer surplus are both non-monotonic in search cost. In contrast, when the number of products is finite, we show that the optimal stopping rule is still stationary, but the optimal depth is not and it depends on the previous search outcomes and the number of unevaluated products. Our simulation shows that the equilibrium prices decrease in the number of products.

报告人简介:俞浩君,上海财经大学国际工商管理学院助理教授。2006年获复旦大学经济学学士学位,2009年获北京大学经济学硕士学位,2015年获南加州大学经济学博士。研究领域包括:产业经济学和实证产业组织。

 

 

 

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