Opinion Dynamics with Higher-Order Bounded Confidence

Wang, Chaoqian

doi:10.3390/e24091300

Cited by 10 publications

(7 citation statements)

References 61 publications

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“…There have been numerous studies of BCMs, which have been generalized in many ways. Recent studies have incorporated phenomena such as media outlets with fixed opinions 37 , polyadic interactions (i.e., interactions between three or more agents) 38,39 , noise 40 , asymmetric confidence intervals 41 , cost of opinion change 42 , agents with heterogeneous activity levels 43 , smooth interaction kernels (in the form of sigmoidal functions) to describe how agents influence each other 44 , opinion repulsion 45 , homophilic adaptivity of network structure 46 , preserving connectivity of the interaction network 47 , and adaptive confidence bounds 48 .…”

Section: Introductionmentioning

confidence: 99%

An ``Opinion Reproduction Number'' for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks

Brooks,

Porter

2024

Preprint

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We study the spreading dynamics of content on networks. To do this, we use a model in which content spreads through a bounded-confidence mechanism. In a bounded-confidence model (BCM) of opinion dynamics, the agents of a network have continuous-valued opinions, which they adjust when they interact with agents whose opinions are sufficiently close to theirs. The employed content-spread model introduces a twist into BCMs by using bounded confidence for the content spread itself. To study the spread of content, we define an analogue of the basic reproduction number from disease dynamics that we call an \emph{opinion reproduction number}. A critical value of the opinion reproduction number indicates whether or not there is an ``infodemic'' (i.e., a large content-spreading cascade) of content that reflects a particular opinion. By determining this critical value, one can determine whether or not an opinion will die off or propagate widely as a cascade in a population of agents. Using configuration-model networks, we quantify the size and shape of content dissemination using a variety of summary statistics, and we illustrate how network structure and spreading model parameters affect these statistics. We find that content spreads most widely when the agents have large expected mean degree or large receptiveness to content. When the amount of content spread only slightly exceeds the critical opinion reproduction number (i.e., the infodemic threshold), there can be longer dissemination trees than when the expected mean degree or receptiveness is larger, even though the total number of content shares is smaller.

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Section: Introductionmentioning

confidence: 99%

An ``Opinion Reproduction Number'' for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks

Brooks,

Porter

2024

Preprint

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“…So if the difference in opinion of two interacting agents is greater than ϵ, they will not be able to change their opinion. This model does not consider other social, psychological or environmental attributes of the agents [4,5,7].…”

Section: Introductionmentioning

confidence: 99%

A mechanism of random opinion fluctuation for the modeling of reflective agents and its influence on the opinion formation in the C/PA relative agreement societies

Abrica-Jacinto,

Kurmyshev

2024

Preprint

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This article explores the mathematical modelling of intricate internal process of agents’ thoughts, highlighting its importance in the opinion formation at both personal and social level. Current models of opinion dynamics often evade the reflective and focus primarily on the reactive agents. With intent to fill this gap, the article introduces an al?gorithm that simulates the reflection process, assigning this capacity to a number of agents. Simulations and comparative analysis on a small?world network reveal that the reflective agents have a notable impact on the dynamics of opinion; in particular, the proportion and activeness of reflective agents influence the breadth and smoothness of the opinion spectrums, and their presence encourages agents in the formation of centrist groups and create greater tolerance of agents to the opinion of others. These results highlight the intricate dynamics and importance of reflective agents in opinion formation.

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“…For problem B, the pertinent literature has extensively investigated the synchronization of bounded confidence models (the meaning of synchronization is the same as the global consensus aforementioned in the field of opinion dynamics); however, the mechanisms behind chaos remain insufficiently understood. Regarding numerical simulation, some scholars have not only explored the sufficient conditions for the formation of global consensus (such as d > 1/2) but have also simulated the number of consensus categories and convergence rates under various parameter conditions [ 29 , 30 ]. These endeavors yield reliable hypotheses and conjectures for subsequent theorem proofs while unearthing critical values and “statistical laws” governing model phase transitions.…”

Section: Introductionmentioning

confidence: 99%

Bounded Confidence and Cohesion-Moderated Pressure: A General Model for the Large-Scale Dynamics of Ordered Opinion

Ding

Yang

Ding

2023

Entropy

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Due to the development of social media, the mechanisms underlying consensus and chaos in opinion dynamics have become open questions and have been extensively researched in disciplines such as sociology, statistical physics, and nonlinear mathematics. In this regard, our paper establishes a general model of opinion evolution based on micro-mechanisms such as bounded confidence, out-group pressure, and in-group cohesion. Several core conclusions are derived through theorems and simulation results in the model: (1) assimilation and high reachability in social networks lead to global consensus; (2) assimilation and low reachability result in local consensus; (3) exclusion and high reachability cause chaos; and (4) a strong “cocoon room effect” can sustain the existence of local consensus. These conclusions collectively form the “ideal synchronization theory”, which also includes findings related to convergence rates, consensus bifurcation, and other exploratory conclusions. Additionally, to address questions about consensus and chaos, we develop a series of mathematical and statistical methods, including the “energy decrease method”, the “cross-d search method”, and the statistical test method for the dynamical models, contributing to a broader understanding of stochastic dynamics.

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Opinion Dynamics with Higher-Order Bounded Confidence

Cited by 10 publications

References 61 publications

An ``Opinion Reproduction Number'' for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks

An ``Opinion Reproduction Number'' for Infodemics in a Bounded-Confidence Content-Spreading Process on Networks

A mechanism of random opinion fluctuation for the modeling of reflective agents and its influence on the opinion formation in the C/PA relative agreement societies

Bounded Confidence and Cohesion-Moderated Pressure: A General Model for the Large-Scale Dynamics of Ordered Opinion

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