Opinion dynamics in social networks with stubborn agents: Equilibrium and convergence rate

Ghaderi, Javad; Srikant, R.

doi:10.1016/j.automatica.2014.10.034

Cited by 280 publications

(187 citation statements)

References 21 publications

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“…For the case of undirected graph G(W ) and special influence weights of arcs a similar estimate for the convergence speed has been obtained in (Ghaderi and Srikant, 2014). Unlike this paper, Theorem 5 deals with a general FJ model, where the matrix W can be arbitrary.…”

Section: Convergence Speed Of the Fj Modelmentioning

confidence: 71%

“…At the same time, the FJ model has been confirmed by experiments with real social groups (Friedkin and Johnsen, 2011;Friedkin et al, 2016a). The FJ model is closely (Friedkin and Johnsen, 2014;Proskurnikov et al, 2016b) and has been given some elegant game-theoretic and electric interpretations (Bindel et al, 2011;Ghaderi and Srikant, 2014;Frasca et al, 2015). In the recent works (Parsegov et al, 2017;Proskurnikov and Tempo, 2017) necessary and sufficient conditions for the stability of the FJ model has been established; these conditions also provide convergence "on average" of its decentralized gossip-based counterpart (Frasca et al, 2013;Ravazzi et al, 2015;Frasca et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Convergence Speed Of the Fj Modelmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…Here, we study the continuous opinion dynamics where the opinion belongs to a bounded interval. Our work is mostly related to [7] in the case of no stubborn agents. However, in [7] the interactions between agents are symmetric and the cost for each agent of differing with its interacting agents is the same.…”

Section: Related Workmentioning

confidence: 99%

“…Our work is mostly related to [7] in the case of no stubborn agents. However, in [7] the interactions between agents are symmetric and the cost for each agent of differing with its interacting agents is the same. Our work is also related to consensus problems [6] in which the question of interest is whether beliefs (some scalar numbers) held by different agents will converge to a common value.…”

Section: Related Workmentioning

confidence: 99%

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Opinion Manipulation in Social Networks

Silva

2017

Static &Amp; Dynamic Game Theory: Foundations &Amp; Applications

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In this work, we are interested in finding the most efficient use of a budget to promote an opinion by paying agents within a group to supplant their true opinions. We model opinions as continuous scalars ranging from 0 to 1 with 1 (0) representing extremely positive (negative) opinion. We focus on asymmetric confidence between agents. The iterative update of an agent corresponds to the best response to other agents' actions. The resulting confidence matrix can be seen as an equivalent Markov chain. We provide simple and efficient algorithms to solve this problem and we show through an example how to solve the stated problem in practice.

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Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent‐Based Modeling Approach

Cai

Sun

et al. 2017

Water Resources Research

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Flood warnings from various information sources are important for individuals to make evacuation decisions during a flood event. In this study, we develop a general opinion dynamics model to simulate how individuals update their flood hazard awareness when exposed to multiple information sources, including global broadcast, social media, and observations of neighbors' actions. The opinion dynamics model is coupled with a traffic model to simulate the evacuation processes of a residential community with a given transportation network. Through various scenarios, we investigate how social media affect the opinion dynamics and evacuation processes. We find that stronger social media can make evacuation processes more sensitive to the change of global broadcast and neighbor observations, and thus, impose larger uncertainty on evacuation rates (i.e., a large range of evacuation rates corresponding to sources of information). For instance, evacuation rates are lower when social media become more influential and individuals have less trust in global broadcast. Stubborn individuals can significantly affect the opinion dynamics and reduce evacuation rates. In addition, evacuation rates respond to the percentage of stubborn agents in a nonlinear manner, i.e., above a threshold, the impact of stubborn agents will be intensified by stronger social media. These results highlight the role of social media in flood evacuation processes and the need to monitor social media so that misinformation can be corrected in a timely manner. The joint impacts of social media, quality of flood warnings, and transportation capacity on evacuation rates are also discussed.

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Opinion dynamics in social networks with stubborn agents: Equilibrium and convergence rate

Cited by 280 publications

References 21 publications

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

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

Opinion Manipulation in Social Networks

Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent‐Based Modeling Approach

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