Harmonizing Predictive Accuracy and Cooperative Outcomes in Game-Theoretic Systems

Abstract

This paper examines the intricate relationship between predictive accuracy and collective welfare in scenarios where forecasts actively influence individual behaviors. We propose a novel framework in which predictions lead to Nash equilibria that can either prioritize precise outcomes or foster broader social benefits two objectives often in tension. Through theoretical insights and computational experiments, we demonstrate that prioritizing accuracy alone frequently entrenches low-cooperation dynamics, while optimizing for social welfare can promote higher levels of cooperation, albeit with compromises in accuracy. To address these trade-offs, we employ multi-objective optimization techniques and identify Pareto-optimal solutions that balance these competing goals. Furthermore, we explore advanced neural network architectures, highlighting the synergistic benefits of combining graph-based models with centralized systems to encourage cooperation. These findings stress the need to account for performative effects in predictive design, ensuring such systems minimize negative externalities. Future work will extend the framework to practical applications and alternate behavioral models for agents.