The hidden action model captures a fundamental problem of principal-agent theory and provides an optimal sharing rule when only the outcome but not the effort can be observed. However, the hidden action model builds on various explicit and also implicit assumptions about the information of the contracting parties. This paper relaxes key assumptions regarding the availability of information included the hidden action model in order to study whether and, if so, how fast the optimal sharing rule is achieved and how this is affected by the various types of information employed in the principal-agent relation. Our analysis particularly focuses on information about the environment and  feasible actions for the agent to carry out the task. For this, we follow an approach to transfer closed-form mathematical models into agent-based computational models . The results show that the extent of information about feasible options to carry out a task only has an impact on performance , if decision-makers are well informed about the environment, and that the decision whether to perform exploration or exploitation when searching for new feasible options only affects performance in specific situations. Having good information about the environment, in contrary, appears to be crucial in almost all situations.
The hidden-action model captures a fundamental problem of principal-agent theory and provides an optimal sharing rule when only the outcome but not the effort can be observed. However, the hidden-action model builds on various explicit and also implicit assumptions about the information of the contracting parties. This paper relaxes key assumptions regarding the availability of information included in the hidden-action model in order to study whether and, if so, how fast the optimal sharing rule is achieved and how this is affected by the various types of information employed in the principal-agent relation. Our analysis particularly focuses on information about the environment and about feasible actions for the agent . We follow an approach to transfer closed-form mathematical models into agent-based computational models and show that the extent of information about feasible options to carry out a task only has an impact on performance if decision makers are well informed about the environment, and that the decision whether to perform exploration or exploitation when searching for new feasible options only affects performance in specific situations. Having good information about the environment, on the contrary, appears to be crucial in almost all situations.
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