论文标题
在非平稳环境中的社会学习
Social Learning in Non-Stationary Environments
论文作者
论文摘要
在做出决定之前，产品或服务的潜在买家倾向于阅读以前消费者撰写的评论。我们考虑具有异质偏好的贝叶斯消费者，根据以前的买家评论，他们依次决定是否购买质量未知质量的商品。质量是多维的，偶尔可能会随着时间而变化。评论也是多维的。在简单的单维和静态环境中，已经知道有关质量的信念会融合其真实价值。我们的论文以几种方式扩展了结果。首先，考虑了多维质量，其次提供了收敛速率，第三，研究了具有不同质量的动力学马尔可夫模型。在这种动态环境中，学习成本显示很小。
Potential buyers of a product or service, before making their decisions, tend to read reviews written by previous consumers. We consider Bayesian consumers with heterogeneous preferences, who sequentially decide whether to buy an item of unknown quality, based on previous buyers' reviews. The quality is multi-dimensional and may occasionally vary over time; the reviews are also multi-dimensional. In the simple uni-dimensional and static setting, beliefs about the quality are known to converge to its true value. Our paper extends this result in several ways. First, a multi-dimensional quality is considered, second, rates of convergence are provided, third, a dynamical Markovian model with varying quality is studied. In this dynamical setting the cost of learning is shown to be small.
