Heterogeneous Collective Opinion Dynamics

This collaborative project introduces a <text style='color: #4cb7feff'><b>Bayesian framework</b></text> for <text style='color: #F9D779'><b>opinion dynamics</b></text>, moving beyond deterministic models to formally <text style='color: #4cb7feff'><b>model uncertainty</b></text> within <text style='color: #4cb7feff'><b>social networks</b></text>. By treating opinions as probability distributions rather than fixed points, we explore how agents update their beliefs when faced with noisy or conflicting information. The research investigates how <text style='color: #4cb7feff'><b>heterogeneity</b></text> in network topology (centrality) and quality of information (certainty) changes the dynamics collective consensus. In this work we combined network science with probabilistic inference to provide a mathematical foundation for understanding how opinion dynamics evolves in complex, decentralized systems, offering key insights for both social modeling and the design of resilient multi-agent AI.

Functional Summary

This research introduces a Bayesian Framework for opinion dynamics, moving beyond deterministic models to formally model uncertainty within social networks. Published in Scientific Reports (Nature).

Simulation of Opinion Evolution on Heterogeneous Networks

Bayesian Inference

Treats opinions as probability distributions (PDFs) rather than fixed points, allowing agents to update beliefs based on the certainty of information.

Network Heterogeneity

Investigates how diversity in topology (centrality) and information quality affects the speed and stability of collective consensus.

Key Insight

We found that uncertainty acts as a buffer against polarization, and that highly central agents can accelerate consensus only if their information certainty matches their social influence.

Opinion Update Logic

graph LR subgraph Agent [Agent Internal State] Prior[Prior Belief P(H)] Uncertainty[Internal Uncertainty] end subgraph Network [Social Interaction] Neighbors[Neighbor Opinions] Trust[Trust Weights] end Neighbors -->|Weighted Aggregation| Likelihood[Likelihood Function P(E|H)] Trust -->|Modulates| Likelihood Prior -->|Bayes Rule| Posterior[Posterior Belief P(H|E)] Likelihood -->|Update| Posterior Uncertainty -->|Controls Plasticity| Posterior Posterior -->|Next Step| Prior

Research Stack

A purely computational research project leveraging scientific computing libraries.

Python

Core simulation language.

NetworkX

Complex network generation (Scale-free, Small-world).

SciPy & NumPy

Probabilistic distributions and array operations.

Matplotlib

High-quality publication plotting.