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The spontaneous formation and subsequent growth, dissolution, merger and competition of social groups bears similarities to physical phase transitions in metastable finite systems. We examine three different scenarios, percolation, spinodal decomposition and nucleation, to describe the formation of social groups of varying size and density. In our agent-based model, we use a feedback between the opinions of agents and their ability to establish links. Groups can restrict further link formation, but agents can also leave if costs exceed the group benefits. We identify the critical parameters for costs/benefits and social influence to obtain either one large group or the stable coexistence of several groups with different opinions. Analytic investigations allow to derive different critical densities that control the formation and coexistence of groups. Our novel approach sheds new light on the early stage of network growth and the emergence of large connected components.
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In this paper, we propose a Boltzmann-type kinetic description of opinion formation on social networks, which takes into account a general connectivity distribution of the individuals. We consider opinion exchange processes inspired by the Sznajd model and related simplifications but we do not assume that individuals interact on a regular lattice. Instead, we describe the structure of the social network statistically, assuming that the number of contacts of a given individual determines the probability that their opinion reaches and influences the opinion of another individual. From the kinetic description of the system, we study the evolution of the mean opinion, whence we find precise analytical conditions under which phase transitions, i.e. changes of sign between the initial and the asymptotic mean opinions, occur. Furthermore, we show that a non-zero correlation between the initial opinions and the connectivity of the individuals is necessary to observe phase transitions. Finally, we validate our analytical results through Monte Carlo simulations of the stochastic opinion exchange processes on the social network.
We introduce a simple model of a growing system with $m$ competing communities. The model corresponds to the phenomenon of defeats suffered by social groups living in isolation. A nonequilibrium phase transition is observed when at critical time $t_c$ the first isolated cluster occurs. In the one-dimensional system the volume of the new phase, i.e. the number of the isolated individuals, increases with time as $Z sim t^3$. For a large number of possible communities the critical density of filled space equals to $rho_c = (m/N)^{1/3}$ where $N$ is the system size. A similar transition is observed for ErdH{o}s-R{e}nyi random graphs and Barab{a}si-Albert scale-free networks. Analytic results are in agreement with numerical simulations.
In a recent work [R. Shojaei et al, Physical Review E 100, 022303 (2019)] the Authors calculate numerically the critical temperature $T_c$ of the balanced-imbalanced phase transition in a fully connected graph. According to their findings, $T_c$ decreases with the number of nodes $N$. Here we calculate the same critical temperature using the heat-bath algorithm. We show that $T_c$ increases with $N$ as $N^{gamma}$, with $gamma$ close to 0.5 or 1.0. This value depends on the initial fraction of positive bonds.
Language can exert a strong influence on human behaviour. In experimental studies, it is for example well-known that the framing of an experiment or priming at the beginning of an experiment can alter participants behaviour. However, few studies have been conducted to determine why framing or priming specific words can alter peoples behaviour. Here, we show that the behaviour of participants in a game-theoretical experiment is driven mainly by social norms, and that participants adherence to different social norms is influenced by the exposure to economic terminology. To explore how these terminology-driven changes impact behavior at the system level, we use established frameworks for modeling collective cooperative behaviour. We find that economic terminology induces a behavioural difference which is larger than that caused by financial incentives in the magnitude usually employed in experiments and simulation. These findings place an increased responsibility on scientists and science communicators, as scientific terminology is increasingly communicated to the general population.
What are the mechanisms by which groups with certain opinions gain public voice and force others holding a different view into silence? And how does social media play into this? Drawing on recent neuro-scientific insights into the processing of social feedback, we develop a theoretical model that allows to address these questions. The model captures phenomena described by spiral of silence theory of public opinion, provides a mechanism-based foundation for it, and allows in this way more general insight into how different group structures relate to different regimes of collective opinion expression. Even strong majorities can be forced into silence if a minority acts as a cohesive whole. The proposed framework of social feedback theory (SFT) highlights the need for sociological theorising to understand the societal-level implications of findings in social and cognitive neuroscience.
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