Exploring Equilibrium Strategies in Network Games with Generative AI

TL;DR

The paper tackles the challenge of applying game theory to large-scale and dynamic settings by proposing an integration with generative AI. It outlines a lifecycle framework where generative AI supports model formulation via digital twins and opponent modeling, solution derivation through agent-based exploration and equilibrium outputs, and strategy improvement by generating diverse environments for robust reinforcement learning. A concrete generative AI-enabled framework is presented for optimizing ML model performance under false data injection attacks, demonstrated through a mobile crowdsensing case using GANs, diffusion models, and LLM-driven problem formulation to explore Nash equilibria and robust strategies. The work also discusses practical challenges—computational cost, real-time responsiveness, and ethical considerations—and outlines directions for future research bridging game theory and generative AI.

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.

Figures (2)

• Figure 1: The generative AI-enabled game theoretical framework for optimizing ML model's performance under false data injection attack. The framework follows a layered architecture, including input layer, processing layer, and output layer. The input layer can formulate game model, providing the digital twin environment and modeling the potential opponent. The processing layer can derive the game solution and evaluate the strategy, providing services on creating game agents and exploring results, deriving equilibrium solutions, and evaluating strategy performance in generated new scenarios. The output layer can transform and output the game results, mainly focusing on transforming game strategies to optimization problem results, and outputting values of variables and objective functions.
• Figure 2: Performance evaluation for the proposed framework. (a) The ML model accuracy performance under different number of synthetic images generated by GAN-based agent and GDM-based agent. (b) The utility convergence process for honest user, GAN-based agent, and GDM-based agent. (c) Equilibrium strategy performance evaluation under newly generated game scenarios. Note that utility functions designed for honest and malicious users are (SVM model's accuracy -- collection cost) and (-SVM model's accuracy -- generation cost), respectively. The game strategy indicates the numbers of images collected or generated by honest and malicious users. The Nash equilibrium means that when other player's strategy remains unchanged, any player (i.e., honest user or malicious user) who unilaterally changes its strategy will not increase its profit. To be detailed, after finishing initial strategy exploration, we iteratively search out the Nash equilibrium by inputting the current exploration results to ChatGPT 4 to acquire the next step exploration direction. When the utilities of the players converge to be steady, the Nash equilibrium solution can be reached.