Opinion Dynamics in Learning Systems

2026-03-12

AI Summary

This study addresses the lack of theoretical understanding regarding the co-evolutionary dynamics between learning systems and opinion formation in social networks. It proposes a unified framework that integrates peer-to-peer opinion interactions with learning system performance into a recursive feedback loop: platform prediction → individual opinions → social network evolution → model update. Theoretical analysis and semi-synthetic simulations based on real-world network structures reveal that this co-evolutionary mechanism can yield novel equilibria. Specifically, conventional prediction objectives exhibit a homogenizing effect that facilitates network-wide consensus, cross-individual spillovers emerge under partial observability, and standard approaches substantially underestimate the impact of node-level interventions. These findings underscore the critical role of performance feedback in shaping opinion dynamics within social networks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Game Theory and Economic Paradigms: Adversarial Learning
• Multiagent Systems: Mechanism Design

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Economics, Online Markets and Human Computation: Social networks and social learning
• Web Mining and Content Analysis: Models for Web evolution

Abstract

We propose and analyze a unified framework that interleaves peer-to-peer opinion dynamics with performative effects of learning systems. While network theory studies how opinions evolve via social connections, and performative prediction examines how learning systems interplay with individuals' opinions, neither captures the emergent dynamics when these forces co-evolve. We model this interplay as a recursive feedback loop: a platform's predictions influence individual opinions, which then evolve through social interactions before forming the training data for the next platform model update. We demonstrate that this co-evolution induces a novel equilibrium that qualitatively differs from standard network equilibria. Specifically, we show that standard predictive objectives act as a ``homogenizing force" driving networks toward consensus even under conditions where classical opinion-dynamics models lead to disagreement. Further, we demonstrate how learning under partial observations creates spillover effects among individuals, even if individuals are not susceptible to peer-influence. Finally, we study a platform that systematically deviates from standard predictive objectives, and demonstrate how classical opinion-dynamics models underestimate the equilibrium response to node-level interventions. We complement our theoretical findings with semi-synthetic simulations on social network data. Combined, our results illuminate performativity as an important, so far neglected, qualifying factor in social networks.

Problem

• opinion dynamics
• performative prediction
• social networks
• learning systems
• feedback loop

Innovation

• opinion dynamics
• performative prediction
• co-evolution
• social networks
• feedback loop