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# Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

*Proceedings of The 35th Uncertainty in Artificial Intelligence Conference*, PMLR 115:207-216, 2020.

#### Abstract

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 gametheoretic 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.