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Modern Trends in Sociophysics

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Statistical Physics".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 10524

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Dr. Krzysztof Malarz

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Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30-059 Kraków, Poland
Interests: complex systems; cellular automata; sociophysics; phase transitions; complex networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Katarzyna Sznajd-Weron

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Department of Theoretical Physics, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Interests: Complex systems; interdyscyplinary statistical physics; phase transitions; critical phenomena; sociophysics; opinion dynamics; complex networks; Monte Carlo simulations

Special Issue Information

Dear Colleagues,

As we all know, in science as in art, certain trends emerge from time to time. Unsurprisingly, sociophysics, like social agent-based modeling, is not what it used to be. Initially, these two closely related fields focused mainly on simple models and so-called artificial societies to understand various intriguing collective phenomena, such as spatial segregation, dissemination of culture, crowd behavior, etc. Has anything changed since then? In part, yes; as time went on, models not only became more complex, but more importantly, their analysis became increasingly sophisticated. Moreover, empirical data began to enter the field of sociophysics more broadly. Another trend that dominates modern sociophysics is complex networks of various kinds, including multilayer and temporal networks. These are particularly useful in modeling opinion dynamics, which is also a very popular topic, not only in sociophysics but in social-agent-based modeling in general. Among the topics related to opinion dynamics, those related to polarization, the spread of fake news or conspiracy theories, or the emergence of echo chambers seem particularly relevant to the modern world. However, from our personal perspective, it is not the topics themselves that should define modern sociophysics, but rather the approach used within the field. It is widely believed that validation with empirical data is the most urgent requirement—what is a model without validation? However, is it not the case that a relatively complex model is always possible to fit to the data? Perhaps the most urgent need should be the reproducibility of the results, or the search for universality across multiple models simultaneously?

By posing the above questions, we encourage you to discuss what should define modern sociophysics, what are the most urgent tasks and what are the greatest weaknesses of sociophysics and social agent-based modeling. We do not limit this volume to specific topics, for trends come and go. Instead, we strongly encourage you to consider your research in a wider context and share your reflections on the wider field.

We look forward to receiving your contributions.

Dr. Krzysztof Malarz
Prof. Dr. Katarzyna Sznajd-Weron
Guest Editors

Manuscript Submission Information

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Keywords

sociophysics
agent-based modeling
multilayer and temporal networks
opinion dynamics
spatial segregation
dissemination of culture
crowd behavior

Published Papers (5 papers)

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Research

25 pages, 2941 KiB

Open AccessArticle

Social Depolarization and Diversity of Opinions—Unified ABM Framework

by Paweł Sobkowicz

Entropy 2023, 25(4), 568; https://doi.org/10.3390/e25040568 - 26 Mar 2023

Cited by 4 | Viewed by 1650

Abstract

Most sociophysics opinion dynamics simulations assume that contacts between agents lead to greater similarity of opinions, and that there is a tendency for agents having similar opinions to group together. These mechanisms result, in many types of models, in significant polarization, understood as separation between groups of agents having conflicting opinions. The addition of inflexible agents (zealots) or mechanisms, which drive conflicting opinions even further apart, only exacerbates these polarizing processes. Using a universal mathematical framework, formulated in the language of utility functions, we present novel simulation results. They combine polarizing tendencies with mechanisms potentially favoring diverse, non-polarized environments. The simulations are aimed at answering the following question: How can non-polarized systems exist in stable configurations? The framework enables easy introduction, and study, of the effects of external “pro-diversity”, and its contribution to the utility function. Specific examples presented in this paper include an extension of the classic square geometry Ising-like model, in which agents modify their opinions, and a dynamic scale-free network system with two different mechanisms promoting local diversity, where agents modify the structure of the connecting network while keeping their opinions stable. Despite the differences between these models, they show fundamental similarities in results in terms of the existence of low temperature, stable, locally and globally diverse states, i.e., states in which agents with differing opinions remain closely linked. While these results do not answer the socially relevant question of how to combat the growing polarization observed in many modern democratic societies, they open a path towards modeling polarization diminishing activities. These, in turn, could act as guidance for implementing actual depolarization social strategies. Full article

(This article belongs to the Special Issue Modern Trends in Sociophysics)

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29 pages, 3673 KiB

Open AccessArticle

Inferring Cultural Landscapes with the Inverse Ising Model

by Victor Møller Poulsen and Simon DeDeo

Entropy 2023, 25(2), 264; https://doi.org/10.3390/e25020264 - 31 Jan 2023

Cited by 4 | Viewed by 2831

Abstract

The space of possible human cultures is vast, but some cultural configurations are more consistent with cognitive and social constraints than others. This leads to a “landscape” of possibilities that our species has explored over millennia of cultural evolution. However, what does this fitness landscape, which constrains and guides cultural evolution, look like? The machine-learning algorithms that can answer these questions are typically developed for large-scale datasets. Applications to the sparse, inconsistent, and incomplete data found in the historical record have received less attention, and standard recommendations can lead to bias against marginalized, under-studied, or minority cultures. We show how to adapt the minimum probability flow algorithm and the Inverse Ising model, a physics-inspired workhorse of machine learning, to the challenge. A series of natural extensions—including dynamical estimation of missing data, and cross-validation with regularization—enables reliable reconstruction of the underlying constraints. We demonstrate our methods on a curated subset of the Database of Religious History: records from 407 religious groups throughout human history, ranging from the Bronze Age to the present day. This reveals a complex, rugged, landscape, with both sharp, well-defined peaks where state-endorsed religions tend to concentrate, and diffuse cultural floodplains where evangelical religions, non-state spiritual practices, and mystery religions can be found. Full article

(This article belongs to the Special Issue Modern Trends in Sociophysics)

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30 pages, 1358 KiB

Open AccessArticle

Vanishing Opinions in Latané Model of Opinion Formation

by Maciej Dworak and Krzysztof Malarz

Entropy 2023, 25(1), 58; https://doi.org/10.3390/e25010058 - 28 Dec 2022

Cited by 3 | Viewed by 1468

Abstract

In this paper, the results of computer simulations based on the Nowak–Szamrej–Latané model with multiple (from two to five) opinions available in the system are presented. We introduce the noise discrimination level (which says how small the clusters of agents could be considered negligible) as a quite useful quantity that allows qualitative characterization of the system. We show that depending on the introduced noise discrimination level, the range of actors’ interactions (controlled indirectly by an exponent in the distance scaling function, the larger the exponent, the more influential the nearest neighbors are) and the information noise level (modeled as social temperature, which increases results in the increase in randomness in taking the opinion by the agents), the ultimate number of the opinions (measured as the number of clusters of actors sharing the same opinion in clusters greater than the noise discrimination level) may be smaller than the number of opinions available in the system. These are observed in small and large information noise limits but result in either unanimity, or polarization, or randomization of opinions. Full article

(This article belongs to the Special Issue Modern Trends in Sociophysics)

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14 pages, 2397 KiB

Open AccessArticle

Conflict Dynamics in Scale-Free Networks with Degree Correlations and Hierarchical Structure

by Eduardo Jacobo-Villegas, Bibiana Obregón-Quintana, Lev Guzmán-Vargas and Larry S. Liebovitch

Entropy 2022, 24(11), 1571; https://doi.org/10.3390/e24111571 - 31 Oct 2022

Viewed by 1551

Abstract

We present a study of the dynamic interactions between actors located on complex networks with scale-free and hierarchical scale-free topologies with assortative mixing, that is, correlations between the degree distributions of the actors. The actor’s state evolves according to a model that considers its previous state, the inertia to change, and the influence of its neighborhood. We show that the time evolution of the system depends on the percentage of cooperative or competitive interactions. For scale-free networks, we find that the dispersion between actors is higher when all interactions are either cooperative or competitive, while a balanced presence of interactions leads to a lower separation. Moreover, positive assortative mixing leads to greater divergence between the states, while negative assortative mixing reduces this dispersion. We also find that hierarchical scale-free networks have both similarities and differences when compared with scale-free networks. Hierarchical scale-free networks, like scale-free networks, show the least divergence for an equal mix of cooperative and competitive interactions between actors. On the other hand, hierarchical scale-free networks, unlike scale-free networks, show much greater divergence when dominated by cooperative rather than competitive actors, and while the formation of a rich club (adding links between hubs) with cooperative interactions leads to greater divergence, the divergence is much less when they are fully competitive. Our findings highlight the importance of the topology where the interaction dynamics take place, and the fact that a balanced presence of cooperators and competitors makes the system more cohesive, compared to the case where one strategy dominates. Full article

(This article belongs to the Special Issue Modern Trends in Sociophysics)

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15 pages, 14830 KiB

Open AccessEditor’s ChoiceArticle

Consensus, Polarization and Hysteresis in the Three-State Noisy q-Voter Model with Bounded Confidence

by Maciej Doniec, Arkadiusz Lipiecki and Katarzyna Sznajd-Weron

Entropy 2022, 24(7), 983; https://doi.org/10.3390/e24070983 - 16 Jul 2022

Cited by 7 | Viewed by 1649

Abstract

In this work, we address the question of the role of the influence of group size on the emergence of various collective social phenomena, such as consensus, polarization and social hysteresis. To answer this question, we study the three-state noisy q-voter model with bounded confidence, in which agents can be in one of three states: two extremes (leftist and rightist) and centrist. We study the model on a complete graph within the mean-field approach and show that, depending on the size q of the influence group, saddle-node bifurcation cascades of different length appear and different collective phenomena are possible. In particular, for all values of

q > 1

, social hysteresis is observed. Furthermore, for small values of

q \in (1, 4)

, disagreement, polarization and domination of centrists (a consensus understood as the general agreement, not unanimity) can be achieved but not the domination of extremists. The latter is possible only for larger groups of influence. Finally, by comparing our model to others, we discuss how a small change in the rules at the microscopic level can dramatically change the macroscopic behavior of the model. Full article

(This article belongs to the Special Issue Modern Trends in Sociophysics)

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