Many-to-many matching and price discrimination
研究了多对多匹配市场中代理人拥有私人信息时的最优匹配规则，揭示了匹配模式如何反映跨边补贴，并给出了可检验的价格方案预测，对理解大型匹配中介（如媒体平台、B2B平台）的定价实践有参考价值。
We study centralized many-to-many matching in markets where agents have private information about (vertical) characteristics that determine match values. Our analysis reveals how matching patterns reflect cross-subsidization between sides. Agents are endogenously partitioned into consumers and inputs. At the optimum, the costs of procuring agents-inputs are compensated by the gains from agents-consumers. We show how such cross-subsidization can be achieved through matching rules that have a simple threshold structure, and deliver testable predictions relating the optimal price schedules to the distribution of the agents' characteristics. The analysis sheds light on the practice of large matching intermediaries, such as media and business-to-business platforms, advertising exchanges, and commercial lobbying firms.