Experimentation and Learning in Repeated Cooperation

We study an agency model, in which the principal has only incomplete information about the agent's preferences, in a dynamic setting. Through repeated interaction with the agent, the principal learns about the agent's preferences and can thus adjust the inventive system. In a dynamic computational model, we compare different learning strategies of the principal when facing different types of agents. The results indicate that better learning of preferences can improve the situation of both parties, but the learning process is rather sensitive to random disturbances.