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Guillaume Deffuant

Publications and source records attributed to Guillaume Deffuant.

21 records · Page 2Linked to original sources

Modelling Group Opinion Shift to Extreme : the Smooth Bounded Confidence Model

We consider the phenomenon of opinion shift to the extreme reported in the social psychology literature. We argue that a good candidate to model this phenomenon can be a new variant of the bounded confidence (BC) model, the smooth BC model which we propose in this paper. This model considers individuals with a continuous opinion and an uncertainty. Individuals interact by random pairs, and attract each other’s opinion proportionally to a Gaussian function of the distance between their opinions. We first show that this model presents a shift to the extreme when we introduce extremists (very convinced individuals with extreme opinions) in the population, even if there is the same number of extremists located at each extreme. This behaviour is similar to the one already identified with other versions of BC model. Then we propose a modification of the smooth BC model to account for the social psychology data and theories related to this phenomenon. The modification is based on the hypothesis of perspective taking (empathy) in the context of consensus seeking.

cond-mat.other↗

Persuasion dynamics

We here discuss a model of continuous opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. We concentrate on the version of the model in the presence of few extremists which might drive the dynamics to generalised extremism. The intricate regime diagram is explained by a combination of meso-scale features involving the first interaction steps

cond-mat.other↗

Interacting Agents and Continuous Opinions Dynamics

We present a model of opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. High thresholds yield convergence of opinions towards an average opinion, whereas low thresholds result in several opinion clusters. The model is further generalised to network interactions, threshold heterogeneity, adaptive thresholds and binary strings of opinions.

cond-mat.dis-nn↗