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Statistical Approaches for Modeling Human Social Systems

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1172

Special Issue Editor


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Guest Editor
Grupo Decisión Multicriterio Zaragoza (GDMZ), Departamento Economía Aplicada, Facultad de Economía y Empresa, Universidad de Zaragoza, Zaragoza, Spain
Interests: applied mathematics; applied statistics; artificial intelligence; sentiment analysis; information flow; cognitive decision making
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Special Issue Information

Dear Colleagues,

Human social systems are inherently complex, shaped by dynamic interactions, collective behaviors, and cognitive processes that influence individual and group decisions. Advances in statistical modeling now allow us to better understand, quantify, and predict these social dynamics using rigorous analytical and computational tools. Entropy is calling for original research submissions for a Special Issue focusing on statistical approaches for modeling human social systems, with special attention to the integration of information-theoretic and entropy-based concepts.

We invite contributions presenting innovative statistical, probabilistic, and data-driven models aimed at explaining or forecasting human and social behavior. Potential topics include—but are not limited to—statistical modeling of sentiment and opinion dynamics in social networks, cognitive decision-making processes, and co-decision frameworks such as shared decision making in health systems. Studies addressing the statistical structure of online interactions, the diffusion of emotions and information in digital communication platforms, and the role of uncertainty and information flow in social coordination are also within scope.

We particularly welcome interdisciplinary work connecting statistics, psychology, cognitive science, computational social science, medicine, and information theory, as well as studies combining statistical physics approaches, Bayesian inference, or entropy-based measures to capture the emergent complexity of human decision-making and social organization.

The purpose of this Special Issue is to highlight high-quality, impactful research advancing our understanding of human social systems through the lens of statistical and information-theoretic modeling, bridging individual cognition and collective phenomena. We welcome contributions addressing the quantitative analysis of collective and cognitive behaviors, including sentiment and opinion dynamics in social networks, decision-making under uncertainty, and shared decision processes such as doctor–patient co-decision in health systems. Interdisciplinary studies that bridge statistics, information theory, cognitive science, and computational social science are particularly encouraged.

Prof. Dr. Jorge Navarro
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • statistical modeling
  • human social systems
  • sentiment analysis and social networks
  • cognitive decision making
  • co-decision models in complex systems
  • entropy and information theory
  • Bayesian and probabilistic inference
  • computational social science

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Published Papers (2 papers)

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Research

34 pages, 2083 KB  
Article
A Public Opinion Propagation Model for Human-Made Disasters Considering Herd Behavior and Psychological Involvement
by Yi Zhang, Ting Ni and Wanjie Tang
Entropy 2026, 28(3), 303; https://doi.org/10.3390/e28030303 - 8 Mar 2026
Viewed by 474
Abstract
This study investigates the dynamics of information diffusion and uncertainty evolution in online public opinion systems under human-made disasters. A variant of the SIR model considering individual psychological involvement and group herd behavior is proposed. The theoretical analysis derives the propagation equilibrium points [...] Read more.
This study investigates the dynamics of information diffusion and uncertainty evolution in online public opinion systems under human-made disasters. A variant of the SIR model considering individual psychological involvement and group herd behavior is proposed. The theoretical analysis derives the propagation equilibrium points and the propagation threshold and further examines the stability of the system. The results indicate that the transmission rate, immunity rate, and herd behavior coefficient are key parameters influencing the dynamics of public opinion propagation. The simulation results validate the theoretical findings and provide a visualization of the sensitivity of the key parameters. Finally, an empirical case study is conducted to verify the effectiveness and applicability of the proposed model. The results indicate that controlling contact rate, reducing herd behavior, and lowering psychological involvement can effectively suppress opinion diffusion, with herd behavior and psychological involvement exerting a greater influence than contact rate on spreaders of the public opinion system. Consequently, mitigating public emotional resonance and herd effects constitutes an effective strategy for managing public opinion in human-made disasters, but reducing herd behavior makes the system relatively more uncertain compared with other scenarios. Finally, managerial implications for public opinion governance in human-made disasters are proposed. The findings enrich the theoretical system of information evolution modeling for complex social systems based on entropy and information theory, offer practical guidance for governments in developing scientific public opinion management strategies, and realize the transformation of public opinion systems from high-entropy disorder to low-entropy order. Full article
(This article belongs to the Special Issue Statistical Approaches for Modeling Human Social Systems)
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20 pages, 3407 KB  
Article
A Prospective Method for the Dynamic Transformation of Structural Balance in Fully Signed Networks
by Zhanyong Jiao, Jiarui Fan, Ruochen Zhang and Dinghan Duan
Entropy 2026, 28(1), 85; https://doi.org/10.3390/e28010085 - 11 Jan 2026
Viewed by 369
Abstract
Structural balance in fully signed networks, integrating both individual attributes and relationships, represents a critical challenge in social computing; however, its dynamic transformation remains underexplored. This study extends structural balance theory by incorporating node attributes and formulating a mathematical framework for optimizing balance [...] Read more.
Structural balance in fully signed networks, integrating both individual attributes and relationships, represents a critical challenge in social computing; however, its dynamic transformation remains underexplored. This study extends structural balance theory by incorporating node attributes and formulating a mathematical framework for optimizing balance dynamics in fully signed networks. A memetic algorithm is designed to achieve structural balance with minimal cost. Evaluations on both synthetic and real-world networks demonstrate the proposed method’s effectiveness, efficiency, and social interpretability. Full article
(This article belongs to the Special Issue Statistical Approaches for Modeling Human Social Systems)
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