Incentive Games and Mechanisms for Risk Management

TL;DR

This paper introduces a game-theoretic framework for designing incentive mechanisms to improve risk management in large organizations, ensuring efficiency and strategy-proofness even with limited information.

Contribution

It develops a novel incentive mechanism design framework using game theory, including algorithms suitable for large-scale, information-limited risk management scenarios.

Findings

Mechanisms satisfy efficiency, preference-compatibility, and strategy-proofness.

Distributed algorithms work under information limitations.

Numerical example demonstrates effectiveness of the approach.

Abstract

Incentives play an important role in (security and IT) risk management of a large-scale organization with multiple autonomous divisions. This paper presents an incentive mechanism design framework for risk management based on a game-theoretic approach. The risk manager acts as a mechanism designer providing rules and incentive factors such as assistance or subsidies to divisions or units, which are modeled as selfish players of a strategic (noncooperative) game. Based on this model, incentive mechanisms with various objectives are developed that satisfy efficiency, preference-compatibility, and strategy-proofness criteria. In addition, iterative and distributed algorithms are presented, which can be implemented under information limitations such as the risk manager not knowing the individual units' preferences. An example scenario illustrates the framework and results numerically. The incentive mechanism design approach presented is useful for not only deriving guidelines but also developing computer-assistance systems for large-scale risk management.