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 infinite-horizon framework by setting participation fees or quantities on both sides. We show that a price-setting platform provider sets a fee lower than the myopically optimal level on at least one side of the market, and on both sides if the two externalities are of approximately equal strenght. If the externality that one side exerts is sufficiently weaker than the externality it experiences, the optimal fee on this side exceeds the myopically optimal level. We obtain analogous results for expected prives when the platform provider chooses quantities. While the optimal policy does not admin closed-form representations in general, we identify special cases in which the undiscounted limit of the model can be solved in closed form.
Two-Sided Market Network Effects Monopoly Experimentation Bayesian Learning Optimal Control