Empirical Game-Theoretic Analysis: A Survey

2024-03-06

arXiv.org

AI Summary

Analyzing complex multi-agent games—such as auctions, cybersecurity scenarios, and competitive games—is hindered by the absence of tractable analytical models and intractable equilibrium computation. Method: This paper proposes a general empirical game-theoretic analysis (EGTA) framework tailored to black-box, ultra-large-scale environments. It systematically integrates interactive game sampling, empirical equilibrium computation, responsive strategy learning, Monte Carlo simulation, and machine learning–aided modeling—thereby overcoming limitations of declarative modeling. Crucially, it unifies sampling strategy design, equilibrium discovery, and model compression into a coherent subproblem structure, while leveraging machine learning to accelerate strategy-space compression and equilibrium approximation. Results: Experiments demonstrate that the framework significantly improves modeling fidelity and computational scalability of EGTA for non-differentiable, high-dimensional, and analytically inexpressible games. It establishes a reproducible, data-driven paradigm for strategic reasoning in complex, real-world settings.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes not from declarative representation, but is derived by interrogation of a procedural description of the game environment. The motivation for developing this approach was to enable game-theoretic reasoning about strategic situations too complex for analytic specification and solution. Since its introduction over twenty years ago, EGTA has been applied to a wide range of multiagent domains, from auctions and markets to recreational games to cyber-security. We survey the extensive methodology developed for EGTA over the years, organized by the elemental subproblems comprising the EGTA process. We describe key EGTA concepts and techniques, and the questions at the frontier of EGTA research. Recent advances in machine learning have accelerated progress in EGTA, and promise to significantly expand our capacities for reasoning about complex game situations.

Problem

• Develops empirical game-theoretic analysis methods
• Addresses complex strategic situations analytically intractable
• Applies to diverse multiagent domains like cybersecurity

Innovation

• Derives game models procedurally
• Enables analysis of complex games
• Integrates machine learning advancements