Incentive mechanism design problem based on gradient dynamics

Abstract

Purpose

The purpose of this paper is to study the incentive mechanism design problem of manager under the assumption of bounded rationality in the theoretical framework of principal‐agent relationship and provide some insights into the relationship between the economic agent's rationality and incentive mechanism.

Design/methodology/approach

The framework of principal‐agent relationship is based on the full rationality of the agents. However, the agents cannot make optimal decisions in the uncertain and imprecise environment. To capture the essence of incentive scheme, bounded rationality is introduced in the model and the gradient dynamics is applied in modelling bounded rational learning behavior. The corresponding single‐task principal‐agent problem is solved to obtain the second‐best contracts and the effort levels. Furthermore, the results are generalized for the multi‐task principal‐agent problem.

Findings

The second‐best contract under the assumption of bounded rationality converges to the optimal contract under the assumption of full rationality over time.

Research limitations/implications

Accessibility of data for empirical research is the main limitation which model will be applied.

Practical implications

A useful reference for managerial decisions.

Originality/value

The new approach of bounded rationality modeling and simulation. This paper is aimed at the owners, enterprisers, managers, and workers.

Citation

Kuiran, S., Tiaojun, X. and Zhaohan, S. (2009), "Incentive mechanism design problem based on gradient dynamics", Kybernetes, Vol. 38 No. 3/4, pp. 481-488. https://doi.org/10.1108/03684920910944191