Matthew J.H. Murphy

I study the optimal design of review systems. A platform learns a product’s quality from user-generated reviews. It faces a trade-off between the informativeness and frequency of reviews. Detailed reviews are individually more informative but less frequently submitted than simple reviews. I characterize the informational content of review systems, which depends on the information in each review and their submission rate. I use this characterization to derive the platform’s optimal review system and show how it varies with reviewers’ preference heterogeneity. In particular, when reviewers are homogeneous the platform isolates “horrible" experiences instead of differentiating between “good" and “bad" experiences.

I study how market structure influences the distribution of entertainment products. Producers compete for consumer attention through horizontal differentiation across “genres". I show that the combination of pricing frictions and ex-ante producer uncertainty over own quality is necessary and sufficient for market inefficiency. When both frictions are present, production is inefficiently concentrated in high-demand, homogeneous, genres. I also study how alternative market structures interact with these frictions. In particular, a monopolist using subscription pricing produces inefficiently diverse content and recommendation systems can improve on the decentralized equilibrium by selectively suppressing content.