Optimal Design for Social Learning
Optimal Design for Social Learning
复制标题
社会学习的优化设计
DOI：
10.2139/ssrn.2600931
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Johannes Horner
中科院分区：
文献类型：
--
作者：
Yeon;Johannes Horner
本文研究了一个用于组织产品社会学习的推荐系统的设计。为了提高早期实验的激励，最优设计通过在产品周期的早期阶段向一小部分代理过度推荐产品（或“垃圾邮件”）来权衡完全透明的社会学习。在最佳方案下，设计师在产品发布后很少发送垃圾邮件，但逐渐增加垃圾邮件的频率，并在产品被认为不值得推荐时完全停止。最佳的推荐系统包括当推荐是公开的时随机触发的垃圾邮件--就像产品评级的情况一样--以及信息“封锁”，然后是一系列垃圾邮件，这时代理商可以选择何时检查推荐。完全透明的建议可能会成为最佳的，如果（社会慈善）设计师不观察代理人的实验成本。
This paper studies the design of a recommender system for organizing social learning on a product. To improve incentives for early experimentation, the optimal design trades off fully transparent social learning by over-recommending a product (or “spamming”) to a fraction of agents in the early phase of the product cycle. Under the optimal scheme, the designer spams very little about a product right after its release but gradually increases the frequency of spamming and stops it altogether when the product is deemed sufficiently unworthy of recommendation. The optimal recommender system involves randomly triggered spamming when recommendations are public -- as is often the case for product ratings -- and an information “blackout” followed by a burst of spamming when agents can choose when to check in for a recommendation. Fully transparent recommendations may become optimal if a (socially-benevolent) designer does not observe the agents’ costs of experimentation.