Novel Multidimensional Models of Opinion Dynamics in Social Networks

Parsegov, Sergei; Proskurnikov, Anton V.; Tempo, Roberto; Friedkin, Noah E.

doi:10.1109/tac.2016.2613905

Cited by 283 publications

(225 citation statements)

References 56 publications

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“…In [29] disagreement is explained as the effect of obstinacy, that is translated into the dependence of any individual's opinion on its initial value. Stubborness as the source of disagreement is also considered in other models, such as [39,42], also in connection with the occurrence of randomized asynchronous interactions [2,25,45]. Another explanation has been proposed to be the presence of contrarians [31] or of negative interactions, i.e.…”

Section: Theorem 8 (Sufficient Conditions For Consensus)mentioning

confidence: 99%

Discontinuities, Generalized Solutions, and (Dis)agreement in Opinion Dynamics

Ceragioli¹,

Frasca²

2018

Control Subject to Computational and Communication Constraints

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Section: Theorem 8 (Sufficient Conditions For Consensus)mentioning

confidence: 99%

Discontinuities, Generalized Solutions, and (Dis)agreement in Opinion Dynamics

Ceragioli¹,

Frasca²

2018

Control Subject to Computational and Communication Constraints

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“…We also examine the extension of the FJ model to the case of time-varying social influence and give sufficient conditions for its stability. The timevarying FJ model can be further extended to the case of multidimensional opinions, representing the agents' positions on several interrelated issues (belief systems); for static FJ model such an extension is discussed in (Parsegov et al, 2017;Friedkin et al, 2016b). In our future works we are going to validate the applicability of the FJ model to opinion dynamics in large-scale online social networks.…”

Section: Discussionmentioning

confidence: 99%

“…The Schur stability is a "generic" condition if at least one prejudiced agent exists, and holds, for instance, for a strongly connected influence networks 2 , as implied by the following lemma (Parsegov et al, 2017). Lemma 1.…”

Section: The Friedkin-johnsen Modelmentioning

confidence: 97%

Opinion evolution in time-varying social influence networks with prejudiced agents

et al. 2017

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Investigation of social influence dynamics requires mathematical models that are "simple" enough to admit rigorous analysis, and yet sufficiently "rich" to capture salient features of social groups. Thus, the mechanism of iterative opinion pooling from (DeGroot, 1974), which can explain the generation of consensus, was elaborated in (Friedkin and Johnsen, 1999) to take into account individuals' ongoing attachments to their initial opinions, or prejudices. The "anchorage" of individuals to their prejudices may disable reaching consensus and cause disagreement in a social influence network. Further elaboration of this model may be achieved by relaxing its restrictive assumption of a time-invariant influence network. During opinion dynamics on an issue, arcs of interpersonal influence may be added or subtracted from the network, and the influence weights assigned by an individual to his/her neighbors may alter. In this paper, we establish new important properties of the (Friedkin and Johnsen, 1999) opinion formation model, and also examine its extension to time-varying social influence networks.

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“…Opinion dynamics in social networks have been modeled in the context of multi-agent network games [8], [9], [35]. In this subsection, we build upon this literature and conceive opinion dynamics as multi-agent proximal dynamics, possibly multidimensional, interdependent, locally constrained, with possibly time-varying social interactions.…”

Section: Applications a Opinion Dynamics In Social Networkmentioning

confidence: 99%

Proximal Dynamics in Multiagent Network Games

Grammatico

2018

IEEE Trans. Control Netw. Syst.

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Abstract-We consider dynamics and protocols for agents seeking an equilibrium in a network game with proximal quadratic cost coupling. We adopt an operator theoretic perspective to show global convergence to a network equilibrium, under the assumption of convex cost functions with proximal quadratic couplings, time-invariant and time-varying communication graph along with convex local constraints, and time-invariant communication graph along with convex local constraints and separable convex coupling constraints. We show that proximal dynamics generalize opinion dynamics in social networks and are applicable to distributed tertiary control in power networks. Advantageously, distributed computation and communication setups allow each agent to keep its own data private and exchange information with selected agents only. Typically, in networked multi-agent systems, the state (or decision) variables of each agent evolve as a result of local decision making, e.g. local optimization subject to private constraints, and distributed communication with some other agents, according to a communication graph. It then follows naturally that the aim of the agents is to reach a collective equilibrium state, where no agent can benefit from updating its state variables. I. INTRODUCTIONLiterature overview: In this paper, we study network games with proximal, hence quadratic, cost coupling between neighboring agents, that are related to the literature of distributed multi-agent equilibrium seeking in network games and distributed multi-agent optimization.Network games among agents with convex compact local constraints have been considered in [14] under the assumption of strongly convex quadratic cost functions and time-invariant communication graph; in [15] [16], under the assumption of differentiable cost functions with Lipschitz continuous gradient, strictly monotone pseudo-gradient game mapping (hence strictly convex cost functions), and undirected, possibly time-varying, communication graph. Multi-agent games with convex compact local and also coupling constraints have been considered in [17] under the assumption of strongly convex twice differentiable cost functions with bounded gradients, with strictly increasing congestion cost term.

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Novel Multidimensional Models of Opinion Dynamics in Social Networks

Cited by 283 publications

References 56 publications

Discontinuities, Generalized Solutions, and (Dis)agreement in Opinion Dynamics

Discontinuities, Generalized Solutions, and (Dis)agreement in Opinion Dynamics

Opinion evolution in time-varying social influence networks with prejudiced agents

Proximal Dynamics in Multiagent Network Games

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