  The hidden-action model provides an optimal sharing rule for situations in
which a principal assigns a task to an agent who makes an effort to carry out
the task assigned to him. However, the principal can only observe the task
outcome but not the agent's actual action, which is why the sharing rule can
only be based on the outcome. The hidden-action model builds on somewhat
idealized assumptions about the principal's and the agent's capabilities
related to information access. We propose an agent-based model that relaxes
some of these assumptions. Our analysis lays particular focus on the
micro-level dynamics triggered by limited access to information. For the
principal's sphere, we identify the so-called Sisyphus effect that explains why
the sharing rule that provides the agent with incentives to take optimal action
is difficult to achieve if the information is limited, and we identify factors
that moderate this effect. In addition, we analyze the behavioral dynamics in
the agent's sphere. We show that the agent might make even more of an effort
than optimal under unlimited access to information, which we refer to as excess
effort. Interestingly, the principal can control the probability of making an
excess effort via the incentive mechanism. However, how much excess effort the
agent finally makes is out of the principal's direct control.
