Strategically Robust Aggregative Games

2026-04-26

AI Summary

This work addresses the challenge of robust decision-making in multi-agent systems, where agents often face uncertainties stemming from incomplete information, computational constraints, or bounded rationality. The authors propose the Strategy-Robust Wardrop Equilibrium (SRWE), which leverages optimal transport theory to construct a behavioral ambiguity set, enabling protection against worst-case population behaviors without explicitly modeling each source of uncertainty. SRWE continuously interpolates between the classical Wardrop equilibrium and maximin safety strategies, revealing a novel coordination mechanism: enhancing robustness simultaneously reduces equilibrium costs for all participants. Theoretical analysis establishes the existence of SRWE, and an efficient algorithm is developed for its computation. Empirical validation in an electric vehicle charging scenario demonstrates that the approach significantly improves decision quality and lowers aggregate system costs.

Technology Category

• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements

Abstract

In many multiagent settings, such as electric vehicle charging and traffic routing, agents must make decisions in the face of uncertain behavior exhibited by others. Often, this uncertainty arises from multiple sources, such as incomplete information, limited computation, or bounded rationality, ultimately impacting the aggregate behavior. To tackle this challenge, we follow recent work on strategically robust game theory and postulate that agents seek protection directly against deviations around the emergent behavior, as opposed to explicitly modeling all sources of uncertainty. Specifically, we propose that each agent protects itself against the worst-case aggregate behavior within an optimal-transport-based ambiguity set centered at the emergent aggregate population behavior. This leads to a novel equilibrium concept, called strategically robust Wardrop equilibrium, that enables one to interpolate between standard Wardrop equilibria (no robustness) and security strategies (maximum robustness). In the setting of convex aggregative games, we establish the existence of a pure strategically robust Wardrop equilibrium and provide tractable computational tools for computing it. Through an application in electric vehicle charging, we demonstrate that strategically robust Wardrop equilibria lead to better decisions, protecting agents against the uncertain aggregate behavior of the population. Remarkably, we also observe that strategic robustness can lead to lower equilibrium costs for all agents, uncovering a "coordination-via-robustification" effect.

Problem

• strategic robustness
• aggregative games
• behavioral uncertainty
• Wardrop equilibrium
• multiagent systems

Innovation

• strategically robust equilibrium
• aggregative games
• optimal transport ambiguity set
• Wardrop equilibrium
• coordination-via-robustification