Delegation and Learning

Authors: Bikramaditya Datta


A principal contracts with an agent to complete a task. The agent’s ability to complete the task is uncertain and is learnt from the agent’s performance in projects that the principal finances. Success however also depends on the quality of the project at hand, and quality is privately observed by the agent who is biased towards implementation. We characterize the optimal sequence of rewards in a relationship that tolerates an endogenously determined finite number of failures and incentivizes the agent to implement only good projects by specifying rewards for success as a function of past failures. The fact that success becomes less likely over time suggests that rewards for success should increase with past failures. However, this means that the agent can earn a rent by deviating and implementing a bad project, which is sure to fail. We show that this rent decreases with past failures and implies that optimal rewards are front-loaded. The optimal contract resembles the arrangements used in venture capital, where entrepreneurs must give up equity share in exchange for further funding following failure.