Mixing beliefs among interacting agents

By Guillaume Deffuant, D. Neau, Frédéric Amblard, G. Weisbuch. Advances in Complex Systems, 2000

2,193 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a9a6d57863f48b7ca7c5e6ec1f914029d937b66d

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In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

Statistical physics of social dynamics (2007, methodology): “In this section we discuss the most popular BC models, i.e., the Deffuant model (Deffuant et al., 2000) and that of Hegselmann-Krause (Hegselmann and Krause, 2002).”
Opinion Dynamics and the Evolution of Social Power in Influence Networks (2015, background): “, see [2,11,22,40]).”
Social Influence and the Collective Dynamics of Opinion Formation (2013, background): “different rules of opinion adaptation, such as imitation [24], averaging over people with similar opinions [25,26], following the”
Opinion Dynamics and Learning in Social Networks (2011, methodology): “…by Clifford and Sud-bury [23] and by Holley and Liggett [60]), or continuous values (as in the DeGroot model [28] and the bounded conﬁdence models of Krause [63] (further developed by Hegselmann and Krause [59], Deffuant, Neau, Amblard and Weisbuch [27] and Weisbuch, Kirman and Herreiner [93])).”
Meet, discuss, and segregate! (2002, background): “A previous publication [24] and a working paper [25] report more complete results on several aspects.”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “[512] Guillaume Deffuant, David Neau, Frederic Amblard, and Gérard Weisbuch.”
Continuous Opinion Dynamics under Bounded Confidence: A Survey (2007, background): “In (9; 10) authors therefore decided to not count clusters with only one agent.”
An Individual‐Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit1 (2005, methodology): “We studied the properties of this model (see Deffuant et al. 2001; Deffuant 2001), in particular, with a population including extremists (Deffuant et al. 2002a).”
Models of Social Influence: Towards the Next Frontiers (2017, methodology): “The parameter µ defines the rate of opinion convergence and is typically constrained to 0 < µ ≤ 0.5 (e.g., De uant et al. 2000).”
A tutorial on modeling and analysis of dynamic social networks. Part I (2017, background): “In the second part of the tutorial, we are going to discuss advanced more advanced models of opinion formation, based on the ideas of bounded con-ﬁdence [92, 151, 152], antagonistic interactions [153– 155] and asynchronous gossip-based interactions [134, 156].”
Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization (2017, background): “This standard model, introduced by [17], remains the starting point for much recent research (see [12]).”
Distributed Control of Robotic Networks (2009, background): “…4: Rendezvous (a) (b) Figure 4.11 The evolution of (S ∞-disk , pll-crcmcntr) with n = 25 robots in 2 dimensions: (a) shows the initial connected network configuration; (b) shows the evolution of the individual agents until rendezvous is achieved. culture (see Deffuant et al., 2000; Axelrod, 1997).”
On the control through leadership of the Hegselmann–Krause opinion formation model (2015, background): “In particular, one of the most representative class of agent-based models explaining phenomena in social sciences are the opinion formation models; see the seminal papers [4, 16, 22, 32] and, for further developments on these models, see Refs.”
Opinion Dynamics in Social Networks With Hostile Camps: Consensus vs. Polarization (2015, background): “[24], [25] or, more generally, biased assimilation [26]: agents readily adopt opinions of like-minded neighbors, accepting the”