Continuous opinion model in small-world directed networks

Gandica, Yérali; Castillo-Mussot, Marcelo del; Vázquez, G. J.; Rojas, Sergio

doi:10.1016/j.physa.2010.08.025

Cited by 39 publications

(33 citation statements)

References 23 publications

(29 reference statements)

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“…For instance, simulations on square lattices and BA random graphs suggest that K depends not only on c, but also on N , when multiple opinion groups persist at equilibrium [26,45]. Simulations on WS random graphs indicate that K depends on both c and network structures, and that the presence of disorder (i.e., random "shortcut" edges) seems to have only a slight effect on convergence time T [43].…”

Section: The Deffuant Model On Various Networkmentioning

confidence: 99%

“…For WS random graphs, the study of the model has centered around opinion groups at equilibrium [43].…”

Section: The Deffuant Model On Various Networkmentioning

confidence: 99%

“…Results for complete graphs and square lattices are similar for large confidence-bound values, except that a few extreme opinions remain on square lattices at equilibrium [30]. The Deffuant model has also been simulated on random graphs generated by Barabási-Albert (BA), Erdős-Rényi (ER), and WattsStrogatz (WS) mechanisms [40,[43][44][45]. However, different assumptions and update rules are often used, and this poses a major barrier for comparing results across different networks.…”

Section: Introductionmentioning

confidence: 99%

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Opinion formation and distribution in a bounded-confidence model on various networks

2018

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In the social, behavioral, and economic sciences, it is an important problem to predict which individual opinions will eventually dominate in a large population, if there will be a consensus, and how long it takes a consensus to form. This idea has been studied heavily both in physics and in other disciplines, and the answer depends strongly on both the model for opinions and for the network structure on which the opinions evolve. One model that was created to study consensus formation quantitatively is the Deffuant model, in which the opinion distribution of a population evolves via sequential random pairwise encounters. To consider the heterogeneity of interactions in a population due to social influence, we study the Deffuant model on various network structures (deterministic synthetic networks, random synthetic networks, and social networks constructed from Facebook data) using several interaction mechanisms. We numerically simulate the Deffuant model and conduct regression analyses to investigate the dependence of the convergence time to equilibrium on parameters, including a confidence bound for opinion updates, the number of participating entities, and their willingness to compromise. We find that network structure and parameter values both have an effect on the convergence time, and for some network topologies, the convergence time undergoes a transition at a critical value of the confidence bound. We discuss the number of opinion groups that form at equilibrium in terms of a confidence-bound threshold for a transition from consensus to multiple-opinion equilibria.

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Section: The Deffuant Model On Various Networkmentioning

confidence: 99%

“…For WS random graphs, the study of the model has centered around opinion groups at equilibrium [43].…”

Section: The Deffuant Model On Various Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Opinion formation and distribution in a bounded-confidence model on various networks

2018

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“…The second point is discussed below in Section 2.2.2. We have previously shown that these adaptations provide notable, and arguably more sociologically representative outcomes for opinion convergence modelling [28], as has subsequent work that also underlines the impact of both a continuous opinion convergence factor, and external determinants [6].…”

Section: Introductionmentioning

confidence: 75%

“…For example, the 'bounded confidence model' of opinion formation with continuous opinions has been studied extensively [1,2,3,4,5,6] . However, the impact of an adaptively changing social network within that framework has only recently been introduced, and it was shown, for example, that convergence to a small number of opinion clusters is more likely when links within the network change adaptively [4,5].…”

Section: Introductionmentioning

confidence: 99%

A model of the effects of authority on consensus formation in adaptive networks: Impact on network topology and robustness

Prettejohn¹,

Berryman²,

McDonnell³

2013

Physica A: Statistical Mechanics and its Applications

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Opinions of individuals in real social networks are arguably strongly influenced by external determinants, such as the opinions of those perceived to have the highest levels of authority. In order to model this, we have extended an existing model of consensus formation in an adaptive network by the introduction of a parameter representing each agent's level of 'authority' based on their opinion relative to the overall opinion distribution. We found that introducing this model, along with a randomly varying opinion convergence factor, significantly impacts the final state of converged opinions and the number of interactions required to reach that state. We also determined the relationship between initial and final network topologies for this model, and whether the final topology is robust to node removals. Our results indicate firstly that the process of consensus formation with a model of authority consistently transforms the network from an arbitrary initial topology to one with distinct measurements in mean shortest path, clustering coefficient, and degree distribution. Secondly, we found that subsequent to the consensus formation process, the mean shortest path and clustering coefficient are less affected by both random and targeted node disconnection. Speculation on the relevance of these results to real world applications is provided. AbstractOpinions of individuals in real social networks are arguably strongly influenced by external determinants, such as the opinions of those perceived to have the highest levels of authority. In order to model this, we have extended an existing model of consensus formation in an adaptive network, by the introduction of a parameter representing each agent's level of 'authority', based on their opinion relative to the overall opinion distribution. We found that introducing this model, along with a randomly varying opinion convergence factor, significantly impacts the final state of converged opinions and the number of interactions required to reach that state. We also determined the relationship between initial and final network topologies for this model, and whether the final topology is robust to node removals. Our results indicate firstly that the process of consensus formation with a model of authority consistently transforms the network from an arbitrary initial topology, to one with distinct measurements in mean shortest path, clustering coefficient, and degree distribution. Secondly, we found that subsequent to the consensus formation process, the mean shortest path and clustering coefficient are less affected by both random and targeted node disconnection. Speculation on the relevance of these results to real world applications is provided.

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Quality control decision of government procurement of elderly care service based on multi‐index fusion of Pythagoras TOPSIS: Perspective of complex network

Zhang

2021

Manage Decis Econ

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In the face of quality problems in government procurement of elderly care services, mastering relationships between quality‐influencing factors and identifying key factors is beneficial for managers to make correct quality control decisions. From the complex networks' perspective, quality‐influencing factors of government purchasing elderly care services are obtained by case analysis, and the influencing factors' network topology is constructed. Importance indexes of nodes are determined with complex network theory, and multi‐indexes are fused by the Pythagoras TOPSIS to evaluate the importance of each factor. The results reveal key factors in the quality of government procurement of elderly care services.

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Continuous opinion model in small-world directed networks

Cited by 39 publications

References 23 publications

Opinion formation and distribution in a bounded-confidence model on various networks

Opinion formation and distribution in a bounded-confidence model on various networks

A model of the effects of authority on consensus formation in adaptive networks: Impact on network topology and robustness

Quality control decision of government procurement of elderly care service based on multi‐index fusion of Pythagoras TOPSIS: Perspective of complex network

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