The impact of variable commitment in the Naming Game on consensus formation

Niu, Xiang; Doyle, Casey; Korniss, G.; Szymański, Bolesław K.

doi:10.1038/srep41750

Cited by 26 publications

(22 citation statements)

References 18 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples include dosage based models of opinion spread [27]. Furthermore, there exist computational models that deal with individuals that are particularly stubborn and difficult to change [28][29][30], similar to the noted population of outliers that would not form an opinion. Finally, future models can be developed using the information gained here; the results showing different thresholds for different data-types, sources, and contexts could be used to build new variants of previously studied models to capture specific facets of social interactions.…”

Section: Discussionmentioning

confidence: 99%

Opinion Formation Threshold Estimates from Different Combinations of Social Media Data-Types

Asher¹,

Caylor²,

Doyle³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

Passive consumption of a quantifiable amount of social media information related to a topic can cause individuals to form opinions. If a substantial amount of these individuals are motivated to take action from their recently established opinions, a movement or public opinion shift can be induced independent of the information's veracity. Given that social media is ubiquitous in modern society, it is imperative that we understand the threshold at which social media data results in opinion formation. The present study estimates population opinion formation thresholds by querying 2222 participants about the number of various social media data-types (i.e., images, videos, and/or messages) that they would need to passively consume to form opinions. Opinion formation is assessed across three dimensions, 1) data-type(s), 2) context, 3) and source. This work provides a theoretical basis for estimating the amount of data needed to influence a population through social media information.

show abstract

Section: Discussionmentioning

confidence: 99%

Opinion Formation Threshold Estimates from Different Combinations of Social Media Data-Types

Asher¹,

Caylor²,

Doyle³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…It was introduced in Ref. [527], where the au- thors analyze the impact of the commitment strength defined as the number of subsequent interactions with speakers with opposite opinions needed for the committed agent to change the opinion. This strength is infinity for the traditional committed agents.…”

Section: Before Interaction After Interaction Amentioning

confidence: 99%

Network resilience

Liu,

Li,

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.

show abstract

“…We conclude this section by recalling some NG models, introduced to investigate semiotics dynamics in the presence of committed agents [84][85][86][87]. Committed agents are individu-als whose opinion cannot be changed or, in other words, they are immune to others' influence.…”

Section: Other Ng Modelsmentioning

confidence: 99%

A Bird's-Eye View of Naming Game Dynamics: From Trait Competition to Bayesian Inference

Marchetti,

Heinsalu,

Patriarca

2020

Preprint

View full text Add to dashboard Cite

The present contribution reviews a set of different versions of the basic naming game model, differing in the underlying topology or in the mechanisms regulating the interactions between agents. We include also a Bayesian naming game model recently introduced, which merges the social dynamics of the basic naming game model with the Bayesian learning framework introduced by Tenenbaum and co-workers. The latter model goes beyond the fixed nature of names and concepts of standard semiotic dynamics models and the corresponding one-shot learning process, by describing dynamically how agents can generalize a concept from a few examples, according to principles of Bayesian inference. CONTENTSI. Introduction 1 II. Language as a game: a timeline 2 III. The basic NG model 3 A. Complex networks 3 B. Basic NG model on fully connected networks 4 C. Basic NG model on regular lattices 6 D. Basic NG model on random graphs and scale-free networks 7 E. Basic NG model on small-world networks 10 F. Basic NG model on small-world geographical networks 11 G. Basic NG model on random geometric graphs 11 IV. Modified NG models 12 A. NG model restricted to two conventions 12 B. Role of feedback in the NG model 13 C. Other NG models 14 V. A Bayesian approach to the NG model 15 A. Bayes' theorem 15 B. The Bayesian learning framework of Tenenbaum 16 C. Bayesian naming game model 17 VI. Conclusion 20 Acknowledgments 20 References 20

show abstract

The impact of variable commitment in the Naming Game on consensus formation

Cited by 26 publications

References 18 publications

Opinion Formation Threshold Estimates from Different Combinations of Social Media Data-Types

Opinion Formation Threshold Estimates from Different Combinations of Social Media Data-Types

Network resilience

A Bird's-Eye View of Naming Game Dynamics: From Trait Competition to Bayesian Inference

Contact Info

Product

Resources

About