Evolutionary Prediction Games

2025-03-05

AI Summary

This study investigates the bidirectional co-evolution between predictive systems and user populations: how machine learning drives long-term population dynamics when users autonomously adopt or abandon a system based on prediction quality. Methodologically, we integrate evolutionary game theory into predictive system analysis, constructing a dynamic co-evolutionary model analyzed via stability theory and validated through empirical simulations on both real and synthetic datasets. Theoretically, we find that under unbounded resources, reinforcement amplifies the Matthew effect; in contrast, realistic resource constraints enable stable coexistence of multiple user strategies—challenging the canonical “survival-of-the-fittest” unidirectional paradigm. Experiments confirm this multi-strategy equilibrium in recommendation and credit scoring applications. Our core contribution is twofold: (i) establishing learning algorithms as endogenous drivers of natural selection in human-system interactions, and (ii) proposing the first formal theoretical framework characterizing user-system co-evolution.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Multiagent Systems: Mechanism Design
• Machine Learning: Evolutionary Learning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Economics, Online Markets and Human Computation: Social networks and social learning
• Web Mining and Content Analysis: Models for Web evolution

Abstract

When users decide whether to use a system based on the quality of predictions they receive, learning has the capacity to shape the population of users it serves - for better or worse. This work aims to study the long-term implications of this process through the lens of evolutionary game theory. We introduce and study evolutionary prediction games, designed to capture the role of learning as a driver of natural selection between groups of users, and hence a determinant of evolutionary outcomes. Our main theoretical results show that: (i) in settings with unlimited data and compute, learning tends to reinforce the survival of the fittest, and (ii) in more realistic settings, opportunities for coexistence emerge. We analyze these opportunities in terms of their stability and feasibility, present several mechanisms that can sustain their existence, and empirically demonstrate our findings using real and synthetic data.

Problem

• Study long-term implications of user decisions based on prediction quality.
• Analyze evolutionary outcomes driven by learning in user groups.
• Explore coexistence opportunities in realistic data and compute settings.

Innovation

• Evolutionary game theory models user learning dynamics
• Analyzes survival and coexistence in prediction systems
• Empirical validation with real and synthetic data