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We study data-driven assistants that provide congestion forecasts to users ofshared facilities (roads, cafeterias, etc.), to support coordination betweenthem, 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 apriori unknown and predictions influence outcomes. Addressing (1), we establishconditions under which self-fulfilling prophecies, i.e., "perfect"(probabilistic) predictions of what will happen, solve the coordination problemin the game-theoretic sense of selecting a Bayesian Nash equilibrium (BNE).Next we prove that such prophecies exist even in large-scale settings whereonly aggregated statistics about users are available. This entails a new(nonatomic) BNE existence result. Addressing (2), we propose two assistantalgorithms that sequentially learn from users' reactions, together withoptimality/convergence guarantees. We validate one of them in a largereal-world experiment.

Coordinating users of shared facilities via data-driven predictive assistants and game theory