Decomposable Principal-Agent Problems

This paper investigates conditions under which the adverse selection principal-agent problem can be decomposed into a collection of pointwise maximization problems. The analysis uses an extension of the type assignment approach to optimal nonuniform pricing, pioneered by Goldman, Leland and Sibley (1984), to derive simple sufficient conditions under which such a decomposition is possible. These conditions do not preclude optimal bunching that arises because virtual surplus functions violate the single-crossing property or participation constraints bind at interior types.

Incentives for Experimenting Agents

We examine a repeated interaction between an agent, who undertakes experiments, and a principal who provides the requisite funding for these experiments. The agent's actions are hidden, and the principal, who makes the offers, cannot commit to future actions. We identify the unique Markovian equilibrium (whose structure depends on the parameters) and characterize the set of all equilibrium payoffs, uncovering a collection of non-Markovian equilibria that can Pareto dominate and reverse the qualitative properties of the Markovian equilibrium. The prospect of lucrative continuation payoffs makes it more expensive for the principal to incentivize the agent, giving rise to a dynamic agency cost. As a result, constrained efficient equilibrium outcomes call for nonstationary outcomes that front-load the agent's effort and that either attenuate or terminate the relationship inefficiently early.Experimentation, Learning, Agency, Dynamic agency, Venture capital, Repeated principal-agent problem

Incentives for Experimenting Agents

We examine a repeated interaction between an agent, who undertakes experiments, and a principal who provides the requisite funding for these experiments. The agent’s actions are hidden, and the principal cannot commit to future actions. The repeated interaction gives rise to a dynamic agency cost -- the more lucrative is the agent’s stream of future rents following a failure, the more costly are current incentives for the agent. As a result, the principal may deliberately delay experimental funding, reducing the continuation value of the project and hence the agent’s current incentive costs. We characterize the set of recursive Markov equilibria. We also find that there are non-Markov equilibria that make the principal better off than the recursive Markov equilibrium, and that may make both agents better off. Efficient equilibria front-load the agent’s effort, inducing as much experimentation as possible over an initial period, until making a switch to the worst possible continuation equilibrium. The initial phase concentrates the agent’s effort near the beginning of the project, where it is most valuable, while the eventual switch to the worst continuation equilibrium attenuates the dynamic agency cost.Experimentation, Learning, Agency, Dynamic agency, Venture capital, Repeated principal-agent problem