产品(数学)
排名(信息检索)
收入
后悔
质量(理念)
订单(交换)
营销
业务
计算机科学
佣金
社会学习
水准点(测量)
情感(语言学)
广告
知识管理
人工智能
心理学
机器学习
数学
哲学
会计
几何学
认识论
沟通
地理
大地测量学
财务
作者
Costis Maglaras,Marco Scarsini,D. W. Shin,Stefano Vaccari
出处
期刊:Operations Research
[Institute for Operations Research and the Management Sciences]
日期:2022-09-26
卷期号:71 (4): 1136-1153
被引量:2
标识
DOI:10.1287/opre.2022.2372
摘要
Optimal Policies for Online Platforms When Social Learning Occurs Before buying products online, consumers read the reviews written by the previous customers. If they buy the product, they write a review themselves. When the product is of unknown quality, consumers learn it over time; that is, social learning occurs. If consumers have various purchase options of similar products of different brands, the platform that they use may affect this social learning by choosing the order in which the products appear on its website. In “Product Ranking in the Presence of Social Learning,” Maglaras, Scarsini, Shin, and Vaccari compare various policies that the platform may adopt, with the goal of maximizing its revenue collected from commission fees for sold items. The criterion to compare the policies is the worst-case regret with respect to a fully informed platform benchmark.
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