A two-step communication opinion dynamics model with self-persistence and influence index for social networks based on the DeGroot model

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

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.

A Comment on and Correction to: Opinion Dynamics in the Presence of Increasing Agreement Pressure

We identify counterexamples to the consensus result given in Semonsen et al. (2018). We resolve the counterexamples by replacing Lemma 5 in the given reference with a novel variation of the Banach fixed-point theorem, which explains both the numerical results in the reference and the counterexample(s) in this note and provides a sufficient condition for consensus in systems with increasing peer-pressure. This work is relevant for other papers that have used the proof technique from Semonsen et al. and establishes the veracity of their claims assuming the new sufficient condition.

A probabilistic linguistic opinion dynamics method based on the DeGroot model for emergency decision-making in response to COVID-19

Emergency decision-making entails a multi-criteria problem with a short period and urgent events, which creates difficulties for decision makers to undertake an optimal decision. To ensure the validity and rationality of decision results, the probabilistic linguistic term set is adopted to represent the evaluation information of experts because it can assign different probabilities or importance to different linguistic terms, which is closely related to human cognition. In addition, to portray the dynamic changes in the emergency decision-making process, this study develops a new dynamics method based on the DeGroot model with probabilistic linguistic information. First, to simulate the transition matrix of probabilistic linguistic opinions, the basic operational rules are defined based on the transformation function and expectation function. Next, three forms of influence matrices incorporating similarity, self-persistence, and authority degrees are constructed, and the consensus conditions of the models are discussed. Then, considering the social networks and incomplete trust relationships between experts, a fourth trust-based influence matrix is devised. A case study of emergency decision-making for assessing response plans to COVID-19 is performed to verify the feasibility and effectiveness of the dynamic method. Furthermore, a sensitivity analysis is conducted. Finally, comparisons with classical methods are performed to illustrate the superiorities of the proposed algorithms.

Cross-platform opinion dynamics in competitive travel advertising: A coupled networks’ insight

Social media platforms have become an important tool for travel advertisement. This study constructs the bounded confidence model to build an improved cross-platform competitive travel advertising information dissemination model based on open and closed social media platforms. Moreover, this study examines the evolution process of group opinions in cross-platform information dissemination with simulation experiments. Results reveal that based on strong relationships, the closed social media platform opinion leaders better guide in competitive travel advertising and can bring more potential consumers to follow. However, being an opinion leader on an open social media platform will not result in more consumer following.

Coevolutionary opinion dynamics with sparse interactions in open-ended societies

Opinion dynamics is a crucial topic in complex social systems. However, existing models rarely study limited information accessibility, sparse interactions, and the coevolution of opinion and an open-ended structure. In this paper, we propose the Sparse COevolutionary Open-Ended (SCOOE) model. We address the sparse interaction limitation through extrinsic collective interaction and intrinsic observation based on incomplete neighborhood information. We also consider the coevolution of opinion and open-ended structure by studying structure-opinion co-dynamics when dissidents are leaving and when newcomers with novel opinions are joining. From an opinion dynamics perspective, we find that the proposed mechanisms effectively form lean and fast decision strategies to reduce conflicts under uncertainty. The model is robust in boosting and enhancing a global consensus with only small odds of extreme results. The structure evolves toward a small-world network. We find that an emergent dialectic relationship exists between community segregation and community cohesion viewed from a structural dynamics perspective. We also study the influence of agent heterogeneity under different cognitive ability distributions.

Communication barrier-incorporated network modeling for interorganizational opinion formation in emergency events

During and after an emergency event, multiple organizations with various specialties are involved in consensus decision-making to reduce the loss of lives and property in a timely manner. However, timely, high-consensus decision-making is challenging due to communication barriers between participating organizations. Thus, this study generalizes a conceptual communication network considering communication barriers by reviewing multiple historical emergencies and proposes a quantitative communication network model by integrating an opinion dynamics model and social network analysis (SNA). An illustrative example is provided by simulating two emergency decision-making scenarios to verify the proposed model. A case study of the 2013 Qingdao oil pipeline explosion is presented to demonstrate the feasibility and applicability of the proposed model. The results of the case study indicate that the proposed model can accurately quantify the impact of communication barriers on the opinion formation time. This research provides a quantitative toolkit for understanding and improving decision-making performance in various emergencies.