Opinion polarization from compression-based decision making where agents optimize local complexity and global simplicity
- 1Centre for Social Issues Research, Department of Psychology, University of Limerick, Limerick, Ireland
- 2Mathematics Applications Consortium for Science and Industry (MACSI), Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland
- 3Lero, the Research Ireland Centre for Software, University of Limerick, Limerick, Ireland
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. Author summary: When people split into opposing groups, such as political parties with differing views on social issues, it is called social polarization and is a growing global concern. Understanding how it emerges, however, remains challenging. In this study, we use computer simulations to explore how simple psychological tendencies can combine to produce complex social patterns. Our model brings together two ideas from psychology: people’s desire to be unique within a group and their tendency to simplify complicated information. We show that these two forces can drive social polarization. Unlike many existing models, individuals in our model continue to adjust their views after clusters form, leading to ongoing variation within and between opinion groups. The model also demonstrates that the way people simplify information, as well as the typical size of social groups, can make polarization either predictable or chaotic. Our results help explain why polarization may happen and how individual thinking shapes collective division.
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BibTeX
@article{dubovskaya2026opinion,
title = {{Opinion polarization from compression-based decision making where agents optimize local complexity and global simplicity}},
author = {Alina Dubovskaya and David J. P. O’Sullivan and Michael Quayle},
journal = {PLOS Complex Systems},
year = {2026},
month = sep,
doi = {10.1371/journal.pcsy.0000115},
url = {https://journals.plos.org/complexsystems/article?id=10.1371/journal.pcsy.0000115},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Opinion polarization from compression-based decision making where agents optimize local complexity and global simplicity
AU - Alina Dubovskaya
AU - David J. P. O’Sullivan
AU - Michael Quayle
PY - 2026
DA - 2026/09/15
JO - PLOS Complex Systems
DO - 10.1371/journal.pcsy.0000115
UR - https://journals.plos.org/complexsystems/article?id=10.1371/journal.pcsy.0000115
AB - 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. Author summary: When people split into opposing groups, such as political parties with differing views on social issues, it is called social polarization and is a growing global concern. Understanding how it emerges, however, remains challenging. In this study, we use computer simulations to explore how simple psychological tendencies can combine to produce complex social patterns. Our model brings together two ideas from psychology: people’s desire to be unique within a group and their tendency to simplify complicated information. We show that these two forces can drive social polarization. Unlike many existing models, individuals in our model continue to adjust their views after clusters form, leading to ongoing variation within and between opinion groups. The model also demonstrates that the way people simplify information, as well as the typical size of social groups, can make polarization either predictable or chaotic. Our results help explain why polarization may happen and how individual thinking shapes collective division.
SN - 2837-8830
ER -