In many engineered systems, agents make decisions under incomplete information, creating opportunities for a planner to influence decentralized behavior through signaling. We study how such signaling can be designed in parallel-network, affine latency congestion games when users may not interpret recommendations using the same beliefs assumed by the planner. To do so, we consider Bayesian congestion games with private recommendations and formulate a robust information design problem in which obedience must hold uniformly over a neighborhood of a nominal prior. This addresses the previously uncharacterized issue of whether obedience itself remains reliable under belief heterogeneity, rather than only under the single prior used at the design stage. We characterize policy-level robustness radii, identify regimes in which the robust obedience region remains nonempty, and analyze the resulting robustness--performance tradeoff through a robust value function whose optimal cost is monotone in the robustness requirement and whose local sensitivity is governed by the active obedience constraints.

翻译：在许多工程系统中，代理在不完全信息下做出决策，这为规划者通过信号传递来影响去中心化行为创造了机会。我们研究了在并行网络、仿射延迟拥塞博弈中，当用户可能不按照规划者所假设的相同信念来解释推荐时，如何设计此类信号。为此，我们考虑了具有私人推荐的贝叶斯拥塞博弈，并提出了一个鲁棒信息设计问题，其中服从性必须在一个名义先验的邻域内一致成立。这解决了此前未被表征的问题：服从性本身在信念异质性下是否仍然可靠，而不仅仅是在设计阶段所使用的单一先验下。我们刻画了策略层面的鲁棒性半径，识别了鲁棒服从区域保持非空的体制，并通过一个鲁棒值函数分析了由此产生的鲁棒性-性能权衡，该函数的最优成本随鲁棒性要求单调变化，且其局部敏感性受主动服从约束支配。