Micro-level dynamics in hidden action situations with limited information
Stephan Leitner, Friederike Wall

TL;DR
This paper explores the micro-level dynamics of hidden action situations with limited information, revealing effects like the Sisyphus effect and excess effort, through an agent-based model that relaxes traditional assumptions.
Contribution
It introduces an agent-based model that relaxes idealized assumptions and analyzes micro-level behavioral dynamics under limited information in hidden action scenarios.
Findings
Identification of the Sisyphus effect hindering optimal incentives
Agents may exert more effort than optimal when information is unlimited
Principal can influence excess effort probability through incentives
Abstract
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…
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