Decision-facilitating information in hidden-action setups: An agent-based approach

TL;DR

This paper explores how different types of information influence the speed and effectiveness of reaching optimal sharing rules in hidden-action principal-agent models using agent-based simulations.

Contribution

It introduces an agent-based modeling approach to analyze the impact of environmental and action feasibility information on hidden-action outcomes, relaxing traditional assumptions.

Findings

Environmental information significantly improves performance in most scenarios.

Feasible action information impacts performance only when environmental knowledge is sufficient.

Exploration versus exploitation decisions affect outcomes in specific contexts.

Abstract

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.