Performance-based pay and limited information access: An agent-based model of the hidden action problem

Models involving human decision-makers often include idealized assumptions, such as rationality, perfect foresight, and access to relevant information. These assumptions usually assure the models' internal validity but, at the same time, might limit the models' power to explain empirical phenomena. This paper addresses the well-known model of the hidden action problem, which proposes an optimal performance-based sharing rule for situations in which a principal assigns a task to an agent and the task outcome is shared between the two parties. The principal cannot observe the action taken by the agent to carry out this task. We introduce an agent-based version of this problem in which we relax some of the idealized assumptions. In the proposed model, the principal and the agent only have limited information access and are endowed with the ability to gain, store and retrieve information from their (finite) memory. We follow an evolutionary approach and analyze how the principal's and the agent's decisions affect their respective utilities, the sharing rule, and task performance over time. The results suggest that the optimal (or a close-to-optimal) sharing rule does not necessarily emerge in all cases. The results indicate that the principal's utility is relatively robust to variations in memory. On the contrary, the agent's utility is significantly affected by limitations in the principal's memory, whereas the agent's memory appears to only have a minor effect.