Criteria for asymptotic clustering of opinion dynamics towards bimodal consensus

Angeli, David; Manfredi, Sabato

doi:10.1016/j.automatica.2019.02.008

Cited by 7 publications

(5 citation statements)

References 25 publications

(34 reference statements)

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“…, γ n ] = L + A, γ i = n j=1 a ij , and L and A are the Laplacian and signed adjacency matrix of the DMs' signed network. By resorting to the graph theory, the willingness of DMs [9], the problem of unilateral DMs [150] and antagonistic and indifference DMs [151] have also been recently addressed through opinion dynamics models.…”

Section: Decision Makers' Behaviormentioning

confidence: 99%

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

et al. 2022

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Section: Decision Makers' Behaviormentioning

confidence: 99%

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

et al. 2022

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“…Clearly, for a cooperative node i if (j, i) is an optimistic (or pessimistic) edge, i is willing to update its states on the basis of the input from j, which has a higher (or lower) value. The idea is similar to the bicolored graph examined in [22] but the multiplex topology was not explicitly defined like this. Next, we consider the Byzantine agents in B, which can apply totally different update rules with the aim to corrupt the multi-agent system.…”

Section: B Resilient Consensus Strategiesmentioning

confidence: 99%

“…The time complexity of the above strategies is dominated by the sorting task, which is effectively done by for instance Quicksort having complexity O(n ln n). Moreover, compared to the consensus seeking over bicolored graphs [22], [24], our systems (3) and ( 4) are not confined to be only continuous-time monotone systems.…”

Section: B Resilient Consensus Strategiesmentioning

confidence: 99%

“…However, in some real-life scenarios, agents interaction is not entirely two-sided. Examples include optimistic/pessimistic agents in opinion models [22], where optimistic one only accepts higher opinions while pessimistic one lower opinions, and unilateral gossip-based algorithms for distributed systems [23]. The idea of asymmetric confidence intervals has been explored in studying consensus for cooperative monotone systems of continuous-time agents [24].…”

Section: Introductionmentioning

confidence: 99%

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Resilient Consensus for Robust Multiplex Networks with Asymmetric Confidence Intervals

Shang

2021

IEEE Trans. Netw. Sci. Eng.

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The consensus problem with asymmetric confidence intervals considered in this paper is characterized by the fact that each agent can have optimistic and/or pessimistic interactions with its neighbors. To deal with the asymmetric confidence scenarios, we introduce a novel multiplex network presentation for directed graphs and its associated connectivity concepts including the pseudo-strongly connectivity and graph robustness, which provide a resilience characterization in the presence of malicious nodes. We develop distributed resilient consensus strategies for a group of dynamical agents with locally bounded Byzantine agents in both continuous-time and discrete-time multi-agent systems. Drawing on our multiplex network framework, much milder connectivity conditions compared to existing works are proposed to ensure resilient consensus. The results are further extended to cope with resilient scaled consensus problems which allow both cooperative and antagonistic agreements among agents. Numerical examples are also exhibited to confirm the theoretical results and reveal the factors that affect the speed of convergence in our multiplex network framework.

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“…In the opinion dynamics model with biased assimilation, how individual biases influence social equilibria was reported in [8]. In the nonlinear opinion dynamics model, sufficient conditions guaranteeing asymptotic convergence of opinions were provided in [1].…”

Section: Introductionmentioning

confidence: 99%

Opinion Dynamics Models with Memory in Coopetitive Social Networks: Analysis, Application and Simulation

Liu¹,

Chai²

2021

Preprint

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In some social networks, the opinion forming is based on its own and neighbors' (initial) opinions, whereas the evolution of the individual opinions is also influenced by the individual's past opinions in the real world. Unlike existing social network models, in this paper, a novel model of opinion dynamics is proposed, which describes the evolution of the individuals' opinions not only depends on its own and neighbors' current opinions, but also depends on past opinions. Memory and memoryless communication rules are simultaneously established for the proposed opinion dynamics model. Sufficient and/or necessary conditions for the equal polarization, consensus and neutralizability of the opinions are respectively presented in terms of the network topological structure and the spectral analysis. We apply our model to simulate Kahneman's seminal experiments on choices in risky and riskless contexts, which fits in with the experiment results.Simulation analysis shows that the memory capacity of the individuals is inversely proportional to the speeds of the ultimate opinions formational.

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Criteria for asymptotic clustering of opinion dynamics towards bimodal consensus

Cited by 7 publications

References 25 publications

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Resilient Consensus for Robust Multiplex Networks with Asymmetric Confidence Intervals

Opinion Dynamics Models with Memory in Coopetitive Social Networks: Analysis, Application and Simulation

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