Design of Platform Reputation Systems : Optimal Information Disclosure

Reputation systems play a central role in a variety of marketplaces to eliminate information asymmetry between sellers and buyers. Despite their crucial role, the design of reputation systems remains a complicated issue. Some platforms try to disclose as much product information as possible through their reputation systems, by encouraging consumers to leave reviews or ratings. In contrast, some platforms disclose only partial product information to the public. Platforms, as the designers of such reputation systems, are thus an additional player added to conventional seller-buyer games. This paper studies the optimal information disclosure in a reputation system from the platform's profit standpoint. In a three-player game, sellers with different base quality decide investment in quality, the platform decides the amount of information about the product quality (i.e., ratings) to disclose through its reputation system, and the future consumer as the receiver makes the purchase decision after observing the information about the product quality. By modeling consumers as Bayesian learners, we specify conditions under which withholding some private information of the sellers can serve the platform's goal of maximizing profits by incentivizing seller investment in quality. We find that, although fully disclosing information can eliminate information asymmetry, it may hinder the sellers' incentive to invest in quality, which hurts the platform's profits. Two main effects are in favor of withholding private information: the quality improving effect, which makes sellers more willing to invest in quality, and the sales increasing effect, which leads to more transactions on the platform