A Bayesian framework for opinion dynamics models

[2508.16539] A Bayesian framework for opinion dynamics models Skip to main content Search Submit Donate Log in Economics > Theoretical Economics arXiv:2508.16539 (econ) [Submitted on 22 Aug 2025 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: A Bayesian framework for opinion dynamics models Authors: Yen-Shao Chen , Tauhid Zaman View PDF HTML (experimental) Abstract: This work introduces a subjective Bayesian framework for the individual update rules used in opinion dynamics models. An individual's initial opinion is represented by the center of a prior belief about an unknown state, and the updated opinion by the posterior mean. After observing a signal, the individual interprets it through a subjective likelihood, which may incorporate perceived bias and noise, and updates the belief by Bayes' rule. Varying the prior and perceived-signal distributions generates four principal response classes: linear updating, saturation, tail rejection, and signal tracking. A sufficiently separated bimodal prior generates local overreaction, mixture signals generate localized attenuation through source attribution, and perceived signal bias generates directional reversal over a finite region. The framework provides Bayesian microfoundations for established response functions and shows that updates often viewed as irrational can be Bayes-consistent under a receiver's subjective beliefs. Subjects: Theoretical Economics (econ.TH) ; Computer Science and Game Theory (cs.GT); Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph) Cite as: arXiv:2508.16539 [econ.TH] (or arXiv:2508.16539v2 [econ.TH] for this version) https://doi.org/10.48550/arXiv.2508.16539 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yen-Shao Chen [ view email ] [v1] Fri, 22 Aug 2025 17:03:12 UTC (120 KB) [v2] Thu, 27 Aug 2026 06:54:21 UTC (112 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: econ.TH < prev | next > new | recent | 2025-08 Change to browse by: cs cs.GT cs.SI econ physics physics.soc-ph References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• A Bayesian framework for opinion dynamics models#677412
• Dynamics-structure interchangeability in binary opinion models on multiplex networks#1064943
• Think Bayes 2nd Edition#2400
• Opinion dynamics and bounded confidence: models, analysis and simulation#96457
• Bayesian Epistemology#740130