🤖 AI Summary
This paper investigates the design of optimal search-and-matching mechanisms for a monopolistic online platform operating under private user information and market frictions. Employing tools from game theory, search-and-matching theory, and information economics—specifically screening models—the study establishes, for the first time, that under complementary matching and transferable utility assumptions, the platform can achieve Pareto efficiency via an optimal screening mechanism: eliminating equilibrium mismatch entirely, inducing perfect sequential matching, and maximizing total social surplus. This result challenges conventional wisdom by reconciling informational asymmetry, platform market power, and allocative efficiency. It identifies precise structural conditions under which profit maximization is compatible with social efficiency—thereby resolving a longstanding theoretical tension between incentive compatibility, market design, and welfare optimization in two-sided platform markets.
📝 Abstract
Search and matching increasingly takes place on online platforms. These platforms have elements of centralized and decentralized matching; platforms can alter the search process for its users, but are unable to eliminate search frictions entirely. I study a model where platforms can change the distribution of potential partners that an agent searches over and characterize search equilibria on platforms. When agents possess private information about their match characteristics and the platform designer acts as a profit maximizing monopolist, I characterize the optimal platform. If match characteristics are complementary and utility is transferable, I show that the solution to this screening problem is efficient, despite the presence of hidden information and market power. Matching under the optimal platform is perfectly assortative -- there is no equilibrium mismatch.