We model a duopoly in which two-sided platforms compete on both sides of a two-sided market. Platforms (or intermediaries) select the quality they offer consumers, and the prices they charge to consumers and firms. In this model, non-trivial competition on both sides induces non-quasiconcave...

I study a simple model of moral hazard with soft information. The risk-averse agent takes an action and she alone observes the stochastic outcome; hence the principal faces a problem of ex post adverse selection. With limited instruments, the principal cannot solve these two problems...

I study a model of moral hazard with soft information: the agent alone observes the stochastic outcome of her action; hence the principal faces a problem of ex post adverse selection. With limited instruments the principal cannot solve these two problems independently; the ex post incentive for...

I study a simple model of moral hazard with soft information. The risk-averse agent takes an action and she alone observes the stochastic outcome; hence the principal faces a problem of ex post adverse selection. With limited instruments, the principal cannot solve these two problems...

I study a model of moral hazard with soft information: the agent alone observes the stochastic outcome of her action; hence the principal faces a problem of ex post adverse selection. With limited instruments the principal cannot solve these two problems independently; the ex post incentive for...

We study pricing by a two-sided platform when it faces moral hazard on the sellers? side. In doing so, we introduce an equilibrium notion of platform reputation in an infinite horizon model. We find that with transaction fees only, the platform cannot eliminate the loss of reputation induced by...

We study a model of moral hazard with soft information: the risk-averse agent takes an action and she alone observes the stochastic outcome; hence the principal faces a problem of ex post adverse selection. High-power contracts may not be appropriate when information is soft. The optimal...