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...

We study the evolution of prices set by duopolists who are uncertain about the perceived degree of product differentiation. Customers sometimes view the products as close substitutes, sometimes as highly differentiated. As the informativeness of the quantities sold increases with the price...

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...

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...

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...

We study price-setting duopolists who are uncertain about the degree to which their products are perceived as differentiated. Customers' sensitivity to price differences varies over time and must be estimated from the quantities sold. The information content of these quantities increases with...