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We combine empirical experimental research on biased argument processing with a computational theory of group deliberation to overcome the micro–macro problem of sociology and to clarify the role of biased processing in debates around energy. We integrate biased processing into the framework of argument communication theory in which agents exchange arguments about a certain topic and adapt opinions accordingly. Our derived mathematical model fits significantly better to the experimentally observed attitude changes than the neutral argument processing assumption made in previous models. Our approach provides new insight into the relationship between biased processing and opinion polarization. Our analysis reveals a sharp qualitative transition from attitude moderation to polarization at the individual level. At the collective level, we find that weak biased processing significantly accelerates group decision processes, whereas strong biased processing leads to a meta-stable conflictual state of bi-polarization that becomes persistent as the bias increases. [ABSTRACT FROM AUTHOR]

In traditional group decision-making models, it is commonly assumed that all decision makers exert equal influence on one another. However, in real-world social networks, such as Twitter and Facebook, certain individuals—known as top persuaders—hold a disproportionately large influence over others. This study formulates the consensus-reaching problem in social network group decision making by introducing a novel framework for predicting top persuaders. Building on social network theories, we develop a social persuasion model that integrates social influence and social status to quantify individuals' persuasive power more comprehensively. Subsequently, we propose a new CRP that leverages the influence of top persuaders. Our simulations and comparative analyses demonstrate that: (1) increasing the number of top persuaders substantially reduces the iterations required to achieve consensus; (2) establishing trust relationships between top persuaders and other individuals accelerates the consensus process; and (3) top persuaders retain a high and stable level of influence throughout the entire CRP rounds. Our research provides practical insights into identifying and strategically guiding top persuaders to enhance the efficiency in consensus reaching and reduce social management costs within social networked environments. [ABSTRACT FROM AUTHOR]

We propose a novel multi-population opinion dynamics model that extends the bounded confidence model to analyze how individual and group interactions influence the emergence of consensus, polarization, or fragmentation. Traditional models often compromise between accuracy and scalability by either overlooking agents' similarities or incorporating detailed agent-level connections at the expense of broader applicability. Our model addresses this challenge by leveraging group affiliations to define agent similarities in a scalable manner. The Wasserstein distance is used to quantify the closeness between sub-populations. The dual nature of the proposed method enables the examination of micro-level (individual-level) interactions and macro-level (group-level) dynamics, offering insights into the reciprocal influence between individual behaviors and group dynamics. We rigorously prove the well-posedness of our models and employ simulations to mimic complex social phenomena, demonstrating the framework's ability to capture the social mechanisms driving public opinion formation. [ABSTRACT FROM AUTHOR]

Increased terrorist propaganda has caused greater violence worldwide. Communities are efficient channels through which terrorist organizations can access target audiences; thus, communities are playing larger roles in terrorist propaganda distribution. However, no previous studies have examined the interactions between terrorists and governments regarding terrorist propaganda in communities from a game-theoretic lens. This article aims to fill this research gap. We first classify the relationships between the communities as independent or interactive. For both cases, we describe the process of terrorist propaganda distribution in communities, highlighting the dependence of the degree of terrorist ideology within a community on the terrorist-organization and government resource allocations; based on this, we build novel sequential game models of the terrorist and the government and obtain the equilibrium solutions. Furthermore, we conduct a comparative study to elucidate the differences in the expected government losses for independent and interactive communities. The outcomes are as follows: (i) the higher the community propagation centrality, the greater the resource expenditure of the government and terrorist organization, and (ii) introducing an interaction network among communities decreases the expected government loss when the network directly destroys the distribution uniformity of the community propagation centralities. Finally, we present a case study to illustrate the usability of the proposed models. This study provides novel insights for governments targeting optimal allocation of intervention resources to communities so as to counter terrorist propaganda distributed by a strategic terrorist. [ABSTRACT FROM AUTHOR]

Bounded confidence and negative influence are two of the most important micro-level mechanisms employed in computational models of opinion dynamics to explain polarization of opinions. However, empirical evidence of both mechanisms is debatable. Two common limitations in existing empirical studies are (1) the limited external validity of laboratory experiments, and (2) the inability in study designs to disentangle negative influence from bounded confidence, as well as from other social influence mechanisms like assimilation. We address both limitations, using the Stochastic Actor-Oriented Model (SAOM) with a longitudinal field data set that tracks adolescents’ social network relations and opinions on a set of issues. Two new SAOM effects are introduced to represent bounded confidence and negative influence, respectively. Results show that for adolescents’ preferences on rap/ hip hop clothing style, the model containing both effects, in addition to assimilative influence from friends, provides a good fit to the data, but only the effects representing negative influence and assimilative influence from friends are statistically significant. The results support that negative influence contributes to explaining observed opinion changes, but lend little weight to bounded confidence. Further simulation studies based on the empirically estimated model show that our model implies only low levels of opinion polarization at the macro-level despite negative influence at the micro-level. We conclude that our approach not only overcomes common limitations of earlier empirical work, but also bridges SAOM and agent-based modeling by offering empirically validated insights into opinion dynamics. [ABSTRACT FROM AUTHOR]

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ABSTRACT We study a Hegselmann–Krause type opinion formation model for a system of two populations. The two groups interact with each other via subsets of individuals, namely, the leaders, and natural time delay effects are considered. By using careful estimates of the system's trajectories, we are able to prove an asymptotic convergence to consensus result. Some numerical tests illustrate the theoretical result and point out some possible applications. [ABSTRACT FROM AUTHOR]

Social media networks have become a powerful platform for shaping public opinion. In such networks, opinions change through complex processes. To better understand this process, we present a novel method to examine the specific dynamics of social media interactions and information spread. In this model, each person has both private and public opinions. Private opinions are continuously adjusted based on others' public opinions and personal biases. In contrast, public opinions are updated only when expressed, shaped by the individual's private opinion and a tendency to conform to widely accepted group views and social pressure. We analyze the model's two-state updating mechanisms and provide conditions for opinion convergence. Our findings on the consensus dynamics are presented, along with implications for social interactions in social media networks. The proposed model is validated by comparing real social media Twitter data to the simulated results. [ABSTRACT FROM AUTHOR]

Starting from a symmetrical multiple-choice individual, I build a sociophysics model of decision-making. Reducing the choices to two and interactions to pairs recovers the Ising model from physics at zero temperature. The associated equilibrium state results from a spontaneous symmetry breaking, with the whole group sharing a unique choice, which is selected at random. However, my focus departs from physics, which aims at identifying the true equilibrium state, discarding any possible impact of the initial conditions, the size of the sample, and the update algorithm used. Memory of past history is erased. In contrast, I claim that dealing with a social system, the history of the system must be taken into account in identifying the relevant social equilibrium state, which is always biased by its history. Accordingly, using Monte Carlo simulations, I explore the spectrum of non-universal equilibrium states of the Ising model at zero temperature. In particular, I show that different initial conditions with the same value of the order parameter lead to different equilibrium states. The same applies for different sizes and different update algorithms. The results indicate that in the presence of a social network composed of agents sharing different initial opinions, it is their interactions that lead them to share a unique choice and not their mere membership in the network. This finding sheds a new light on the emergence of echo chambers, which appear to be the end of a dynamical process of opinion update and not its beginning with a preferential attachment. Furthermore, polarization is obtained as a side effect of the random selection of the respective unanimous choices of the various echo chambers within a social community. The study points to social media exchange algorithms, which are purely technical levers independent of the issue and opinions at stake, to tackle polarization by either hindering or accelerating the completion of symmetry breaking between agents. [ABSTRACT FROM AUTHOR]

Due to the discrepancy between an individual's expressed opinion and private opinion in many social situations, this paper aims to study the convergence and bipartite consensus of expressed and private opinion (EPO) model on signed directed network $ \mathcal {G}(W) $ G (W) without satisfying common strong connectivity. First, by constructing an enlarged network $ \mathcal {G}(P) $ G (P) of $ \mathcal {G}(W) $ G (W) and studying the properties of CSCCs in $ \mathcal {G}(P) $ G (P) , we establish some general convergence criteria for the EPO model. Second, we explore the relationship between the final private opinion, final expressed opinion and initial opinion of an individual and prove that the self-confidence of an individual can influence the gap between his final expressed opinion and group pressure. Third, by structural balance theory, we establish the bipartite consensus criterion of the EPO model. Finally, numerical simulations are presented to illustrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

Social-media platforms have created new ways for individuals to keep in touch with others, share their opinions and join the discussion on different issues. Traditionally studied by social science, opinion dynamic has attracted the attention from scientists in other fields. The formation and evolution of opinions is a complex process affected by the interplay of different elements that incorporate peer interaction in social networks and the diversity of information to which each individual is exposed. In addition, supplementary information can have an important role in driving the opinion formation and evolution. And due to the character of online social platforms, people can easily end an existing follower-followee relationship or stop interacting with a friend at any time. Taking a step in this direction, we propose the OG–IC model which considers the dynamic of both opinion and relationship in this paper. It not only considers the direct influence of friends but also highlights the indirect effect of group when individuals are exposed to new opinions. And it allows nodes which represent users of social networks to slightly adjust their own opinion and sometimes redefine friendships. A novel problem in social network whose purpose is simultaneously maximizing both the diversity of supplementary information that individuals access to and the influence of supplementary information on individual's existing opinion is formulated. This problem is proved to be NP-hard and its objective function is neither submodular nor supermodular. However, an algorithm with approximate ratio guarantee is designed based on the sandwich framework. And the effectiveness of our algorithm is experimentally demonstrated on both synthetic and real-world data sets. [ABSTRACT FROM AUTHOR]

In this text, we consider Gärdenfors' conceptual spaces that are separable Hilbert spaces. In particular, the results we obtained apply to finite-dimensional Euclidean spaces. Our main contribution can be formulated as a combination of the theory of opinion dynamics with the theory of conceptual spaces. This combination, in turn, leads us to propose a new model for the time evolution of conceptual spaces. To achieve this goal, we propose some extension of the multidimensional opinion dynamics model of Parsegov, Proskurnikov, Tempo and Friedkin to opinions with values in Hilbert spaces. [ABSTRACT FROM AUTHOR]

We present an opinion dynamics model framework discarding two common assumptions in the literature: (a) that there is direct influence between beliefs of neighboring agents, and (b) that agent belief is static in the absence of social influence. Agents in our framework learn from random experiences which possibly reinforce their belief. Agents determine whether they switch opinions by comparing their belief to a threshold. Subsequently, influence of an alter on an ego is not direct incorporation of the alter's belief into the ego's but by adjusting the ego's decision-making criteria. We provide an instance from the framework in which social influence between agents generalizes majority rules updating. We conduct a sensitivity analysis as well as a pair of experiments concerning heterogeneous population parameters. We conclude that the framework is capable of producing consensus, polarization and fragmentation with only assimilative forces between agents which typically, in other models, lead exclusively to consensus. [ABSTRACT FROM AUTHOR]

Image contrast enhancement is an essential process that improves the visibility of many features that may remain hidden due to low-contrast conditions arising from environmental causes, limitations of the device, or the wrong setting of the camera. This paper introduces a new technique of image contrast enhancement that combines insights from social sciences and image processing. In this model, the intensity of each pixel represents the opinion of an individual, and all the neighboring pixels interact by influencing each other. The algorithm operates to first increase the similarity of those pixels in the regions where pixels maintain similar intensities and, second, to amplify the differences in regions where differences exist. This process increases the contrast in regions with significant differences and reduces variation in uniform regions, hence enhancing clarity in the visual information and details of the image. The effectiveness and high performance of the proposed method are evaluated by a variety of experiments conducted on different image datasets using different quality indexes. The results obtained after experimentation highlight the superiority of the approach with respect to the state-of-the-art techniques of contrast enhancement. [ABSTRACT FROM AUTHOR]

Large-scale studies indicate that the distinct approach to opinion fusion employed by extreme agents exerts a more potent influence on overall opinion evolution when compared to regular agents. The presence of extreme agents within the network tends to undermine the development of opinion neutrality, which is harmful to the guidance of online public opinion. Notably, prior research often overlooks the existence of opinion extreme agents in social networks. However, existing researches seldom consider the time sunk cost in the evolution of opinions. Building upon this foundation, we introduce a temporal dimension to the opinion evolution, integrating the time sunk cost with the opinion evolution process. Furthermore, we devise an agent partitioning method that categorizes agents into four states based on their opinion values: watch state, subjective state, firm state, and extreme state, with extreme state agents generally expressing radical opinions. We constructed an agent network based on the phenomenon of time sunk costs and proposed a model for the evolution of extreme opinions in this network. Our study found that the information sharing among extreme agents significantly influences the extremization of opinions in various networks. After restricting the exchange of opinions on extreme agents, the number of extreme agents in the network decreased by 40% to 50% compared to the initial situation. Additionally, we also discovered that imposing restrictions on extreme agents in the early stages can help increase the possibility of network opinions moving towards neutral positions. When restriction of extreme agents(REA) was performed at the beginning of the experiment compared to REA in the midway of the experiment, the final number of extreme state agents decreased by 15.57%. The results show that extreme agents have a great influence on the spread and evolution of extreme opinions on platforms. [ABSTRACT FROM AUTHOR]

This paper studies the bounded confidence model on growing fully-mixed populations. In this model, in addition to the usual opinion clusters, significant secondary clusters of smaller size appear systematically, while those secondary clusters appear erratically and include much fewer agents when the population is fixed. Through simulations, we derive the bifurcation diagram of the growing population model and compare it to the diagram obtained with an evolving probability density instead of agents, and with their equivalent having a fixed population. Our tests, when changing the usual bounded confidence function into a smooth bounded confidence function, suggest that these secondary clusters are mainly generated by a different mechanism when the population is growing than when it is fixed. [ABSTRACT FROM AUTHOR]

In bounded confidence opinion dynamics, agents only consider other agents when their opinions are sufficiently close. In many works, the confidence bound is assumed as a homogeneous one. However, agents in different situations may be with different confidence bounds in real life. In this work, the correlation of degrees and confidence bounds is introduced into modified Hegselmann–Krause model on complex networks and we mainly focus on whether the population can reach consensus, where all agents hold a same opinion. The number of opinion clusters, the relative size of the largest cluster and the probability of reaching consensus are the measures of the opinion cluster profile. The numerical simulation shows that the existence of the correlation promotes the probability of reaching consensus and even in the case where some agents are with very small confidence bounds. On scale-free and Erdös–Rényi random networks, the negative correlation supports the consensus much more than the positive correlation case. However, two correlations show the same effect on small-world networks, and this effect is also observed in scale-free and Erdös–Rényi random networks when the average degree becomes very large. [ABSTRACT FROM AUTHOR]

The issue of opinion sharing and formation has received considerable attention in the academic literature, and a few models have been proposed to study this problem. However, existing models are limited to the interactions among nearest neighbors, with those second, third, and higher-order neighbors only considered indirectly, despite the fact that higher-order interactions occur frequently in real social networks. In this paper, we develop a new model for opinion dynamics by incorporating long-range interactions based on higher-order random walks that can explicitly tune the degree of influence of higher-order neighbor interactions. We prove that the model converges to a fixed opinion vector, which may differ greatly from those models without higher-order interactions. Since direct computation of the equilibrium opinion is computationally expensive, which involves the operations of huge-scale matrix multiplication and inversion, we design a theoretically convergence-guaranteed estimation algorithm that approximates the equilibrium opinion vector nearly linearly in both space and time with respect to the number of edges in the graph. We conduct extensive experiments on various social networks, demonstrating that the new algorithm is both highly efficient and effective. [ABSTRACT FROM AUTHOR]