Managing asymmetric information effects in decision making: task complexity-based model

Abstract

This paper proposes a formal model to manage the impact of asymmetric information in decision-making by using principal-agent problems in which an agent (who has incomplete information) must decide to perform or not perform a task on behalf of the principal. After performing a complex (simple) task, the agent underrates (overrates) his competence. As a consequence of underestimation, a competent agent may decide to stop performing the task henceforth. The agent infers his competence from his productivity on a performed task. However, the productivity depends on both the agent's competence and the task complexity. To avoid this situation, the company appoints a mentor (fully informed superior agent) who can determine the task complexity and assess the agent's competence. Accordingly, the mentor matches the task complexity perfectly with the agent's competence. In cases where the mentor and the junior have different preferences, the mentor may not confess all information to the agent. Nevertheless, the mentor desires the agent to fulfill the task. This paper proposes a solution for all of these situations by using a mathematical model. The model assesses the agent's competence based on his productivity and the mentor's appraisal and assists the agent in making the right decision.