Exploring Equilibrium Strategies in Network Games with Generative AI

TL;DR

This paper reviews how generative AI can enhance game theory applications, addressing traditional limitations and proposing a framework for optimizing models against false data attacks with promising future research directions.

Contribution

It introduces a novel framework integrating generative AI with game theory, improving model formulation, solution derivation, and strategy optimization in complex scenarios.

Findings

Generative AI improves game theory model formulation.

The framework enhances strategy optimization against false data attacks.

Case study demonstrates effectiveness of the proposed approach.

Abstract

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development.