We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase efficiency of such collective systems. Key questions are: (1) when and how much can (accurate) predictions help for coordination, and (2) which assistant algorithms reach optimal predictions? First we lay conceptual ground for this setting where user preferences are a priori unknown and predictions influence outcomes. Addressing (1), we establish conditions under which self-fulfilling prophecies, i.e., "perfect" (probabilistic) predictions of what will happen, solve the coordination problem in the game-theoretic sense of selecting a Bayesian Nash equilibrium (BNE). Next we prove that such prophecies exist even in large-scale settings where only aggregated statistics about users are available. This entails a new (nonatomic) BNE existence result. Addressing (2), we propose two assistant algorithms that sequentially learn from users' reactions, together with optimality/convergence guarantees. We validate one of them in a large real-world experiment.

中文翻译：

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通过数据驱动的预测助手和博弈论协调共享设施的用户

我们研究数据驱动的助手，为共享设施（道路、自助餐厅等）的用户提供拥堵预测，以支持他们之间的协调，并提高此类集体系统的效率。关键问题是：（1）（准确）预测何时以及在多大程度上可以帮助协调，以及（2）哪些辅助算法可以达到最佳预测？首先，我们为这种设置奠定了概念基础，其中用户偏好是先验未知的，预测会影响结果。针对 (1)，我们建立了条件，在这些条件下，自我实现的预言，即对将要发生的事情的“完美”（概率）预测，解决了选择贝叶斯纳什均衡 (BNE) 的博弈论意义上的协调问题。接下来，我们证明即使在只有有关用户的汇总统计数据的大规模设置中也存在此类预言。这需要一个新的（非原子的）BNE 存在结果。针对 (2)，我们提出了两种辅助算法，它们可以从用户的反应中顺序学习，同时保证最优性/收敛性。我们在大型真实世界实验中验证了其中之一。