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

TL;DR

This paper explores how data-driven predictive assistants can improve coordination among users of shared facilities by leveraging game theory and machine learning, demonstrating their effectiveness through theoretical analysis and real-world experiments.

Contribution

It introduces a game-theoretic framework for predictive assistants, proves the existence of perfect predictions in large-scale settings, and develops algorithms with proven convergence and optimality.

Findings

Self-fulfilling predictions can solve coordination problems.

Existence of perfect predictions in large-scale settings.

Validated one algorithm in a real-world experiment.

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 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.