Coordinating users of shared facilities via data-driven predictive assistants and game theory
This addresses coordination inefficiencies for users of shared facilities, offering a novel game-theoretic approach with practical validation, though it builds incrementally on existing methods.
The paper tackles the problem of coordinating users of shared facilities like roads or cafeterias by using data-driven assistants that provide congestion forecasts, establishing conditions where accurate predictions can solve coordination problems as Bayesian Nash equilibria and proposing algorithms with convergence guarantees validated in a real-world experiment.
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.