# Leveraging Uncertainty in Collective Opinion Dynamics with Heterogeneity

arXiv: https://arxiv.org/abs/2402.03354 · HTML: https://mlsanity.com/paper/2402.03354/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2024.

## Built on by (most cited first, 3 of 3)

- [A Robotics-Inspired Scanpath Model Reveals the Importance of Uncertainty and Semantic Object Cues for Gaze Guidance in Dynamic Scenes](https://mlsanity.com/paper/2408.01322/) (2024)
- [Emergent Heterogeneous Swarm Control Through Hebbian Learning](https://mlsanity.com/paper/2507.11566/) (2025)
- [No Plan, Yet Human: A Reactive Robotics Model Predicts Human Planning Failures on a Clinical Task](https://mlsanity.com/paper/2605.16514/) (2026)

## Abstract (arXiv)

Natural and artificial collectives exhibit heterogeneities across different dimensions, contributing to the complexity of their behavior. We investigate the effect of two such heterogeneities on collective opinion dynamics: heterogeneity of the quality of agents' prior information and of centrality in the network, i.e., the number of immediate neighbors. To study these heterogeneities, we not only consider them in our model, proposing a novel network generator with heterogeneous centrality, but also introduce uncertainty as an additional dimension. By quantifying the uncertainty of each agent, we provide a mechanism for agents to adaptively weigh their individual against social information. As uncertainties develop according to the interactions between agents, they capture information on heterogeneities. Therefore, uncertainty is a relevant additional observable in the study of complex collective opinion dynamics that we use to show the bidirectional relationship of heterogeneous centrality and information. Furthermore, we demonstrate that uncertainty-driven adaptive weighting leads to increased accuracy and speed of consensus, especially under heterogeneity, and provide guidelines for avoiding performance-decreasing errors in uncertainty modeling. These opportunities for improved performance and observability suggest the importance of uncertainty both for the study of natural and the design of artificial heterogeneous systems.