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Models of economic decision makers often include idealized assumptions, suchas rationality, perfect foresight, and access to all relevant pieces ofinformation. These assumptions often assure the models' internal validity, but,at the same time, might limit the models' power to explain empirical phenomena.This paper is particularly concerned with the model of the hidden actionproblem, which proposes an optimal performance-based sharing rule forsituations in which a principal assigns a task to an agent, and the actiontaken to carry out this task is not observable by the principal. We follow theagentization approach and introduce an agent-based version of the hidden actionproblem, in which some of the idealized assumptions about the principal and theagent are relaxed so that they only have limited information access, areendowed with the ability to gain information, and store it in and retrieve itfrom their (limited) memory. We follow an evolutionary approach and analyze howthe principal's and the agent's decisions affect the sharing rule, taskperformance, and their utility over time. The results indicate that the optimalsharing rule does not emerge. The principal's utility is relatively robust tovariations in intelligence, while the agent's utility is highly sensitive tolimitations in intelligence. The principal's behavior appears to be driven byopportunism, as she withholds a premium from the agent to assure the optimalutility for herself.

Limited intelligence and performance-based compensation: An agent-based model of the hidden action problem