Cooperation in partly observable networked markets

We present a model of repeated games in large two-sided networks between clients and agents in the presence of third-party observability networks via which clients share information about past transactions. The model allows us to characterize cooperation networks networks in which each agent cooperates with every client that is connected to her. To this end, we show that: [1] the incentives of an agent a to cooperate depend only on her beliefs with respect to her local neighborhood a subnetwork that includes agent a and is of a size that is independent of the size of the entire network; and [2] when an agent a observes the network structure only partially, the incentives of a to cooperate can be calculated as if the network was a random tree with agent a at its root. Our characterization sheds light on the welfare costs of relying only on repeated interactions for sustaining cooperation, and on how to mitigate such costs.