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

A new study from researchers Dubovskaya, O'Sullivan, and Quayle introduces a novel agent-based model that simulates opinion polarization by combining two psychological 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. Both drives are evaluated using Shannon entropy, a measure of information complexity. The model reproduces real-world patterns, such as the emergence of distinct heterogeneous opinion clusters, and crucially, opinions continue to evolve after clusters form, unlike many existing models where opinions become fixed once groups solidify.

Computational experiments reveal that polarization emerges when local group sizes are moderate (consistent with Dunbar's number, around 150 individuals), while smaller groups cause fragmentation and larger ones hinder distinct cluster formation. Higher cognitive compression increases unpredictability in opinion dynamics, while lower compression produces more consistent group structures. The model demonstrates how simple psychological rules can generate complex, realistic social behavior, advancing understanding of polarization in human societies. The work is published on arXiv (2604.18755) and spans physics, multiagent systems, and adaptation research.