Collective dynamics of belief evolution under cognitive coherence and social conformity
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Abstract (arXiv)
Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying framework that connects cognitive and social forces together in order to study the dynamics of societal belief evolution. Each individual is endowed with a network of interacting beliefs that evolves through interaction with other individuals in a social network. The adoption of beliefs is affected by both internal coherence and social conformity. Our framework explains how social instabilities can arise in otherwise homogeneous populations, how small numbers of zealots with highly coherent beliefs can overturn societal consensus, and how belief rigidity protects fringe groups and cults against invasion from mainstream beliefs, allowing them to persist and even thrive in larger societies. Our results suggest that strong consensus may be insufficient to guarantee social stability, that the cognitive coherence of belief-systems is vital in determining their ability to spread, and that coherent belief-systems may pose a serious problem for resolving social polarization, due to their ability to prevent consensus even under high levels of social exposure. We therefore argue that the inclusion of cognitive factors into a social model is crucial in providing a more complete picture of collective human dynamics.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 2.
5 papers.
- 2024A semantic embedding space based on large language models for modelling human beliefscited by 9
- 2023Sherlock Holmes Doesn't Play Dice: The mathematics of uncertain reasoning when something may happen, that one is not even able to figure outcited by 1
- 2025Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Taskscited by 1
- 2021Your most telling friends: Propagating latent ideological features on Twitter using neighborhood coherencecited by 0
- 2025Coordination Requires Simplification: Thermodynamic Bounds on Multi-Objective Compromise in Natural and Artificial Intelligencecited by 0
- 2025Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks7 Oct 2025
- 2025Coordination Requires Simplification: Thermodynamic Bounds on Multi-Objective Compromise in Natural and Artificial Intelligence27 Sep 2025
- 2024A semantic embedding space based on large language models for modelling human beliefs13 Aug 2024
- 2023Sherlock Holmes Doesn't Play Dice: The mathematics of uncertain reasoning when something may happen, that one is not even able to figure out1 Sep 2023
- 2021Your most telling friends: Propagating latent ideological features on Twitter using neighborhood coherence12 Mar 2021