标题： 偏好测量误差，推荐系统中的集中度以及说服力 显示英文标题

标题： Preference Measurement Error, Concentration in Recommendation Systems, and Persuasion

摘要： 基于噪声偏好测量的算法推荐在推荐系统中很普遍。 本文讨论这种推荐对市场集中度和不平等的影响。 二元类型表示统计上的多数和少数，通过统计实验被噪声地揭示。 两个群体的可实现效用和推荐份额可以作为贝叶斯说服问题来分析。 虽然在任意噪声结构下，与完全信息市场相比集中度的影响是模糊的，但在对称噪声下，集中度增加，消费者福利变得更加不平等。 我们定义了对称统计实验，并在对这类实验的限制下分析了说服，这可能具有独立的兴趣。 显示英文摘要

摘要： Algorithmic recommendation based on noisy preference measurement is prevalent in recommendation systems. This paper discusses the consequences of such recommendation on market concentration and inequality. Binary types denoting a statistical majority and minority are noisily revealed through a statistical experiment. The achievable utilities and recommendation shares for the two groups can be analyzed as a Bayesian Persuasion problem. While under arbitrary noise structures, effects on concentration compared to a full-information market are ambiguous, under symmetric noise, concentration increases and consumer welfare becomes more unequal. We define symmetric statistical experiments and analyze persuasion under a restriction to such experiments, which may be of independent interest.