Opinion polarization from compression-based decision making where agents optimize local complexity and global simplicity

2026-04-20

AI Summary

This study investigates how individual cognitive preferences drive social opinion polarization. By developing an agent-based model that, for the first time, integrates the mechanisms of optimal distinctiveness—seeking local diversity—and cognitive compression—simplifying global information—the authors quantify the trade-off between these two processes using Shannon entropy. The model reveals that opinion clusters evolve dynamically rather than remaining fixed during polarization, and that moderate local group sizes are most conducive to polarized outcomes. High levels of cognitive compression increase system unpredictability, whereas low compression yields stable group structures. The results successfully reproduce heterogeneous opinion clusters observed in real-world settings and link conditions for polarization to Dunbar’s number, offering a novel perspective on how group size influences the emergence of polarization.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Knowledge Representation and Reasoning: Preferences
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Understanding social polarization requires integrating insights from psychology, sociology, and complex systems science. Agent-based modeling provides a natural framework to combine perspectives from different fields and explore how individual cognition shapes collective outcomes. This study introduces a novel agent-based model that integrates two cognitive and social mechanisms: the desire to be unique within a group (optimal distinctiveness theory) and the tendency to simplify complex information (cognitive compression). In the model, virtual agents interact in pairs and decide whether to adopt each other's opinions by balancing two opposing drives: maximizing opinion diversity within their local social group while simplifying the overall opinion landscape, with both evaluated using Shannon entropy. We show that the combination of these mechanisms can reproduce real-world patterns, such as the emergence of distinct heterogeneous opinion clusters. Moreover, unlike many existing models where opinions become fixed once opinion groups form, individuals in our model continue to adjust their opinions after clusters emerge, leading to ongoing variation within and between opinion groups. Computational experiments reveal that polarization emerges when local group sizes are moderate (consistent with Dunbar's number), while smaller groups cause fragmentation and larger ones hinder distinct cluster formation. Higher cognitive compression increases unpredictability, while lower compression produces more consistent group structures. These results demonstrate how simple psychological rules can generate complex, realistic social behavior and advance understanding of polarization in human societies.

Problem

• opinion polarization
• agent-based modeling
• optimal distinctiveness
• cognitive compression
• Shannon entropy

Innovation

• agent-based modeling
• cognitive compression
• optimal distinctiveness
• opinion polarization
• Shannon entropy