Mixing beliefs among interacting agents

Abstract

We present a model of opinion dynamics in which agents adjust continuous opinions as a result of random binary encounters whenever their difference in opinion is below a given threshold. High thresholds yield convergence of opinions towards an average opinion, whereas low thresholds result in several opinion clusters: members of the same cluster share the same opinion but are no longer influenced by members of other clusters.

Connections between the Sznajd model with general confidence rules and graph theory

“…One of these areas is sociophysics that studies how assumptions about the behavior and social interactions of people in a "microcospic level" creates emerging social behaviors, like opinion propagation, consensus formation, properties of elections, how wealth is distributed in society, among other topics. Typical approaches include modeling using deterministic cellular automata, Monte Carlo simulations of models derived from ferromagnetic models (usually Ising and Potts), mean-field approaches and diffusion-reaction processes [1][2][3][4][5][6][7][8].…”

“…In all these variations, the most defining aspect of the Sznajd model is that it gives a greater convincing power to bigger groups of agreeing agents. Even though the importance of this effect has been known by psychologists since the 1950s [11], it is often overlooked in other opinion propagation models, for the sake of simplicity (this happens, for example, in the voter and in the Deffuant models [1,3]). …”

“…In a recent work [12], we took the bounded confidence rule (that roughly says that people are only allowed to change opinions in a smooth way) that is common to many opinion propagation models [3,4,13], including the Sznajd model, and we generalized it to model biases and prejudices * timpa@if.usp.br † prado@if.usp.br in discrete opinion models (these generalized rules will be called by the umbrella term confidence rules). We applied this generalization to the Sznajd model and studied mainly the case with three opinions.…”

Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

“…The previous social interaction models (Deffuant et al, 2000;Javarone & Squartini, 2014;Li et al, 2012;Chau et al, 2014;Das, Gollapudi & Munagala, 2014;Fang, Zhang & Thalmann, 2013;Li et al, 2011) do not assign nodes (i.e., individuals or social agents) the basic properties of humans, i.e., humans evolve, learn, react, and adapt in time. The reason for the simplicity behind the existing models is twofold: first, the state-of-the-art Opinion representation types where the larger nodes (labeled with S) represent stubborn agents (or opinion sources) which can also have any value for opinion, with the property that their opinion value never changes.…”

“…However, such studies offer limited predictability and realism because they are generally based on opinion interaction models which use either fixed thresholds (Deffuant et al, 2000;Javarone & Squartini, 2014), or thresholds evolving according to simple probabilistic processes that are not driven by the internal state of the social agents (Fang, Zhang & Thalmann, 2013;Deng, Liu & Xiong, 2013). To mitigate these limitations, we reveal new dynamical features of opinion spreading.…”