The role of noise and initial conditions in the asymptotic solution of a bounded confidence, continuous-opinion model
arXiv:1208.2618 · doi:10.1007/s10955-012-0635-2
Abstract
We study a model for continuous-opinion dynamics under bounded confidence. In particular, we analyze the importance of the initial distribution of opinions in determining the asymptotic configuration. Thus, we sketch the structure of attractors of the dynamical system, by means of the numerical computation of the time evolution of the agents density. We show that, for a given bound of confidence, a consensus can be encouraged or prevented by certain initial conditions. Furthermore, a noisy perturbation is added to the system with the purpose of modeling the free will of the agents. As a consequence, the importance of the initial condition is partially replaced by that of the statistical distribution of the noise. Nevertheless, we still find evidence of the influence of the initial state upon the final configuration for a short range of the bound of confidence parameter.
References in corpus (4)
Cited by in corpus (12)
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- UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on Graphs
- Approach to consensus in models of continuous-opinion dynamics: a study inspired by the physics of granular gases