Bayesian Updating Rules in Continuous Opinion Dynamics Models

Abstract

In this article, I investigate the use of Bayesian updating rules applied to modeling social agents in the case of continuos opinions models. Given another agent statement about the continuous value of a variable $x$, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a Uniform distribution. This represents the idea the other agent might have no idea about what he is talking about. The effect of updating only the first moments of the distribution will be studied. and we will see that this generates results similar to those of the Bounded Confidence models. By also updating the second moment, several different opinions always survive in the long run. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.

14 pages, 5 figures, presented at SigmaPhi2008

References in corpus (5)

• Statistical physics of social dynamics
• Continuous Opinions and Discrete Actions in Opinion Dynamics Problems
• Persuasion dynamics
• Mobility and Social Network Effects on Extremist Opinions
• How to convince others ? Monte Carlo simulations of the Sznajd model

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• Ideologically Motivated Biases in a Multiple Issues Opinion Model
• Mathematical measures of societal polarisation
• A middle option for choices in the Continuous Opinions and Discrete Actions model
• A Bayesian Framework for Opinion Updates
• Opinion dynamics model based on cognitive biases
• Network generation and evolution based on spatial and opinion dynamics components
• Fuzzy Opinion Networks: A Mathematical Framework for the Evolution of Opinions and Their Uncertainties Across Social Networks
• Person-to-person opinion dynamics: an empirical study using an online game
• Studying on Opinion Evolution by Hamilton-Jacobi Equation
• Opinions and dark energy: A mechanism of long-range repulsion in a toy opinion particle model
• Frustration, glassy behavior and dynamical annealing in societies of Neural Networks