Many-to-Many Matching on a Resource-Sharing Platform

This paper studies the problem of a monopolistic platform which matches agents with private types representing their heterogeneous skills and resources. An agent finds a positive value in subscription to the platform when matched with another agent who has a resource which complements his skill. When the platform uses the divide-and-conquer pricing strategy, we study the properties of incentive compatible mechanisms in relation to the reciprocal property of the complementary relationships among the agents’ types, and identify when such a mechanism becomes optimal in the limit as the market expands in its size.