We characterize a monopolist's optimal offer of service plans when only informed customers know already at the contracting stage whether their demand is high or low, while uninformed customers may learn their demand only after incurring some costs, if at all. While informed customers purchase...

We study optimal experimentation by a monopolistic platform in a two-sided market framework. The platform provider faces uncertainty about the strength of the externality each side is exerting on the other. It maximizes the expected present value of its profit stream in a continuous-time...

We characterize a monopolist's optimal offer of service plans when only informed customers know already at the contracting stage whether their demand is high or low, while uninformed customers may learn their demand only after incurring some costs, if at all. While informed customers purchase...

We study optimal experimentation by a monopolistic platform in a two-sided market framework. The platform provider faces uncertainty about the strength of the externality each side is exerting on the other. It maximizes the expected present value of its profit stream in a continuous-time...

We study optimal experimentation by a monopolistic platform in a two-sided market. The platform provider is uncertain about the strength of the externality each side is exerting on the other. Setting participation fees on both sides, it gradually learns about these externalities by observing...

Lecture on the first SFB/TR 15 meeting, Gummersbach, July, 18 - 20, 2004: We develop a general theoretical framework of trade on a platform on which buyers and sellers interact. The platform may be owned by a single large, or many small independent or vertically integrated intermediaries. We...

We study optimal experimentation by a monopolistic platform in a two-sided mar- ket. The platform provider faces uncertainty about the strength of the externality each side is exerting on the other. It maximizes the expected present value of its profit stream in a continuous-time...