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.

翻译：在许多多智能体场景（如电动汽车充电和交通路线规划）中，智能体需在面临其他智能体行为不确定性的情况下做出决策。这种不确定性往往源自多重因素，例如信息不完整、计算资源有限或有限理性，最终影响系统的聚合行为。为应对这一挑战，我们借鉴策略性稳健博弈论的最新研究成果，提出智能体应直接针对涌现行为周围的偏差寻求保护，而非显式建模所有不确定性来源。具体而言，我们建议每个智能体通过基于最优传输的模糊集（以涌现的种群聚合行为为中心）来保护自身免受最坏情况聚合行为的影响。由此引出一种新型均衡概念——策略性稳健沃德罗普均衡，该均衡可实现在标准沃德罗普均衡（无稳健性）与安全策略（最大稳健性）之间进行插值。针对凸聚合博弈情境，我们证明了纯策略策略性稳健沃德罗普均衡的存在性，并提供了可解的计算工具。通过电动汽车充电的应用案例，我们验证了策略性稳健沃德罗普均衡能引导更优决策，有效保护智能体免受种群不确定聚合行为的影响。值得关注的是，我们还观察到策略性稳健性可降低所有智能体的均衡成本，揭示了"通过稳健化实现协调"的效应。