The principal-agent problem with adaptive players

In this study we show predictions made by the standard principal-agent theory may not hold when knowlege assumptions are relaxed. Conventional principal-agent models assume players are completely rational: they know their own and other player's utilities and probabilities of all states of nature. In reality, players must make decisions without such knowledge.We define a simple version of the principal-agent game and examine it using object-oriented computer simulation. Player learning is modeled with a statistical learning model. Our results show that even this simple game combined with standard learning assumptions results in complex behavior. Expectations of both the principal and the agents are crucial in determining the system outcomes. Expectations and lack of prior knowledge make it possible for the principal to converge on suboptimal behavior or not converge on a consistent behavior at all. The same attributes in the agents make it possible for the principal to drive expectations down and thus get higher effort for lower reward.This study contributes a more robust understanding of the principal-agent model and its application to incentive design.