# Biased Opinion Dynamics: When the Devil Is in the Details

arXiv: https://arxiv.org/abs/2008.13589 · HTML: https://mlsanity.com/paper/2008.13589/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Majority Opinion Diffusion in Social Networks: An Adversarial Approach](https://mlsanity.com/paper/2012.03143/) (2020)
- [UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on Graphs](https://mlsanity.com/paper/2502.11519/) (2025)
- [Random Majority Opinion Diffusion: Stabilization Time, Absorbing States, and Influential Nodes](https://mlsanity.com/paper/2302.06760/) (2023)
- [On the Role of Memory in Robust Opinion Dynamics](https://mlsanity.com/paper/2302.08600/) (2023)
- [Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward](https://mlsanity.com/paper/2601.13122/) (2026)

## Abstract (arXiv)

We investigate opinion dynamics in multi-agent networks when a bias toward one of two possible opinions exists; for example, reflecting a status quo vs a superior alternative. Starting with all agents sharing an initial opinion representing the status quo, the system evolves in steps. In each step, one agent selected uniformly at random adopts the superior opinion with some probability α, and with probability 1 - α it follows an underlying update rule to revise its opinion on the basis of those held by its neighbors. We analyze convergence of the resulting process under two well-known update rules, namely majority and voter. The framework we propose exhibits a rich structure, with a non-obvious interplay between topology and underlying update rule. For example, for the voter rule we show that the speed of convergence bears no significant dependence on the underlying topology, whereas the picture changes completely under the majority rule, where network density negatively affects convergence. We believe that the model we propose is at the same time simple, rich, and modular, affording mathematical characterization of the interplay between bias, underlying opinion dynamics, and social structure in a unified setting.