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Computation
Special Issues
Computational Social Science and Complex Systems—2nd Edition
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Computational Social Science and Complex Systems—2nd Edition

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Social Science".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 8581

Editors

Dr. Minzhang Zheng

E-Mail
Guest Editor
Physics Department, George Washington University, Washington, DC 20052, USA
Interests: complex systems; network science; systems biology and computational social science
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro D. Manrique

E-Mail Website
Guest Editor
Department of Physics, Florida Polytechnic University, Lakeland, FL 33805, USA
Interests: complex systems; non-equilibrium physics; networks; biophysics; math modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social and technological revolutions, such as the internet and social media, have profoundly transformed how humans interact, leading to the development of disciplines such as computing and information technology. We now have access to previously unimaginable amounts of information and high-resolution dynamical data that other sciences can only imagine. From the movements of individuals to the continuous activity in social networks, a challenge for the social and computational sciences is to extract relevant information from these massive amounts of data and unravel the mechanisms that drive its complex dynamics. Computational social science is an emerging discipline in developing and applying computational methods to deal with complex, large-scale, human behavioral data. This interdisciplinary field has attracted great interest among social scientists, computer scientists, and statistical physicists alike.

This Special Issue is devoted to presenting recent developments in the computational and mathematical techniques of data extraction and visualization, analysis, and modeling of complex social structures and bringing a new understanding to the field of computational social sciences. The topics of this Special Issue include but are not limited to:

  • Computer simulation applications in social systems;
  • Social media and social network analysis;
  • Application of big data and artificial intelligence in social science;
  • Social math and modeling;
  • Progress of complex systems;
  • Computational modeling of cognition;
  • Ethics and computational social science.

Dr. Minzhang Zheng
Dr. Pedro D. Manrique
Guest Editors

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation 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 1800 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

  • computational social science
  • complex systems
  • big data
  • social networks
  • machine learning
  • natural language processing

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Related Special Issue

Published Papers (6 papers)

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Research

20 pages, 3532 KB  
Article
Collective Attention Beyond Institutions: A Precision-Based Account of Digital Inference
by Asokan Vasudevan and Samseer Rasak Habsa
Computation 2026, 14(8), 170; https://doi.org/10.3390/computation14080170 - 29 Jul 2026
Viewed by 266
Abstract
Contemporary discussions of collective attention in digital environments often presuppose its existence without specifying the conditions under which it emerges, stabilizes, or fragments. This paper reconceptualizes collective attention not as shared mental focus or institutionally coordinated practice, but as an emergent socio-technical phenomenon [...] Read more.
Contemporary discussions of collective attention in digital environments often presuppose its existence without specifying the conditions under which it emerges, stabilizes, or fragments. This paper reconceptualizes collective attention not as shared mental focus or institutionally coordinated practice, but as an emergent socio-technical phenomenon grounded in the regulation of inference under uncertainty. Drawing on predictive processing, attention is defined as a structuring effort that governs how environmental information is received to support action-guiding inference. The paper develops a precision-based framework distinguishing two ideal-typical regimes: organic attention, which arises when precision remains responsive to uncertainty; and mechanistic attention, which emerges when precision is decoupled from temporal engagement density and redirected by engagement-optimizing architectures—a phenomenon conceptualized as precision hijacking. The framework is operationalized through two system-level indicators—Collective Free Energy (CFE) and Precision Alignment Index (PAI)—applied to temporal interaction data from Wikipedia talk pages. Empirical analysis of Wikipedia talk pages (N = 379,978 articles, N = 191,372 contributors) reveals a collective attention regime characterized by substantial CFE (41,649,821.60), moderate PAI (0.089), and a highly skewed precision distribution (Median = 0.000). Results demonstrate that collective attention can stabilize in minimally institutionalized digital ecologies, exhibiting bounded collective free energy and structured precision alignment. The weak and complex correlation between temporal engagement and precision (Pearson r = 0.059, Spearman ρ = −0.172) suggests that these dimensions are partially coupled but moderated by other factors. These findings show that institutionalization enhances stability but is not a necessary condition for collective attentional emergence. The paper contributes a diagnostic framework for analyzing digital attention economies and offers new resources for the governance of collective sense-making. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems—2nd Edition)
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19 pages, 567 KB  
Article
Online Point-of-Interest Recommendations in Data Streams
by Giannis Christoforidis and Apostolos N. Papadopoulos
Computation 2026, 14(3), 73; https://doi.org/10.3390/computation14030073 - 20 Mar 2026
Viewed by 696
Abstract
In recent years, social networks have shown a great influx of new users and traffic. As their popularity grows, so does the interest in researching ways to process the information available, in order to produce useful knowledge. One direction is making personalized recommendations [...] Read more.
In recent years, social networks have shown a great influx of new users and traffic. As their popularity grows, so does the interest in researching ways to process the information available, in order to produce useful knowledge. One direction is making personalized recommendations based on users’ preferences and on their social behavior and related characteristics in general. Static recommendations, however, are proven to be highly inaccurate, since as time progresses, people tend to change their preferences, making different decisions than the ones predicted previously. This calls for an adaptive algorithm that shifts according to the changes in preferences and habits of the users. Handling the stream of information is challenging, as the new data can severely change the recommendations to many users. In this work, we propose a novel streaming Point-of-Interest recommendation algorithm that explicitly incorporates location-aware features into its dynamic update mechanism, enabling continuous adaptation to newly arriving data. The proposed approach is experimentally evaluated based on real-life data sets containing the network structure as well as check-in information. The results demonstrate high accuracy, achieving at the same time significant performance gains with respect to runtime costs compared to conventional approaches. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems—2nd Edition)
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22 pages, 7389 KB  
Article
Shared Nodes of Overlapping Communities in Complex Networks
by Vesa Kuikka, Kosti Koistinen and Kimmo K. Kaski
Computation 2025, 13(12), 295; https://doi.org/10.3390/computation13120295 - 17 Dec 2025
Viewed by 949
Abstract
Overlapping communities are key characteristics of the structure and function analysis of complex networks. Shared or overlapping nodes within overlapping communities can either form subcommunities or act as intersections between larger communities. Nodes at the intersections that do not form subcommunities can be [...] Read more.
Overlapping communities are key characteristics of the structure and function analysis of complex networks. Shared or overlapping nodes within overlapping communities can either form subcommunities or act as intersections between larger communities. Nodes at the intersections that do not form subcommunities can be identified as overlapping nodes or as part of an internal structure of nested communities. To identify overlapping nodes, we apply a threshold rule based on the number of nodes in the nested structure. As the threshold value increases, the number of selected overlapping nodes decreases. This approach allows us to analyse the roles of nodes considered overlapping according to selection criteria, for example, to reduce the effect of noise. We illustrate our method by using three small and two larger real-world network structures. In larger networks, minor disturbances can produce a multitude of slightly different solutions, but the core communities remain robust, allowing other variations to be treated as noise. While this study employs our own method for community detection, other approaches can also be applied. Exploring the properties of shared nodes in overlapping communities of complex networks is a novel area of research with diverse applications in social network analysis, cybersecurity, and other fields in network science. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems—2nd Edition)
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34 pages, 6219 KB  
Article
Decision-Making and Data Sharing in Smart Catering: An Evolutionary Game Approach
by Jiping Xu, Shuaishuai Cao, Zhaoyang Wang, Chongchong Yu and Minzhang Zheng
Computation 2025, 13(10), 235; https://doi.org/10.3390/computation13100235 - 5 Oct 2025
Viewed by 1468
Abstract
With the rapid advancement of the Internet and big data, data sharing has become pivotal for enhancing operational efficiency and user experience across industries. In the restaurant sector, the emergence of smart kitchens has accelerated digital transformation, underscoring the critical importance of data [...] Read more.
With the rapid advancement of the Internet and big data, data sharing has become pivotal for enhancing operational efficiency and user experience across industries. In the restaurant sector, the emergence of smart kitchens has accelerated digital transformation, underscoring the critical importance of data sharing. In this study, we investigate the evolutionary dynamics among four key stakeholders in the smart kitchen ecosystem: data providers, data-sharing platforms, data consumers, and regulators. We develop a four-party evolutionary game model to analyze the strategic interactions and behavioral evolution of each participant, applying replicator dynamics and Lyapunov stability theory. Our findings reveal that (1) data providers’ willingness to supply high-quality data is strongly influenced by platform incentives; (2) platforms’ adoption of data governance mechanisms depends on associated governance costs; (3) regulatory subsidies contribute significantly to system stability; and (4) increased financial support for regulators promotes favorable system evolution. This work offers both theoretical insights and practical guidance for data sharing in smart kitchens, providing a novel perspective on digital transformation within the restaurant industry. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems—2nd Edition)
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19 pages, 1612 KB  
Article
Exploring Dynamic Behavior in a Competition Duopoly Game Based on Corporate Social Responsibility
by A. A. Elsadany, Abdullah M. Adawi and A. M. Awad
Computation 2025, 13(6), 131; https://doi.org/10.3390/computation13060131 - 2 Jun 2025
Cited by 1 | Viewed by 1198
Abstract
This study investigates dynamic behaviors within a competition Cournot duopoly framework incorporating consumer surplus, and social welfare through the bounded rationality method. The distinctive aspect of the competition game is the incorporation of discrete difference equations into the players’ optimization problems. Both rivals [...] Read more.
This study investigates dynamic behaviors within a competition Cournot duopoly framework incorporating consumer surplus, and social welfare through the bounded rationality method. The distinctive aspect of the competition game is the incorporation of discrete difference equations into the players’ optimization problems. Both rivals seek to achieve optimal quantity outcomes by maximizing their respective objective functions. The first firm seeks to enhance the average between consumer surplus and its profit, while the second firm focuses on its profit optimization with a social welfare component. The game map features four fixed points, with one being the Nash equilibrium point at the intersection of marginal objective functions. Our analysis explores equilibrium stability, dynamic complexities, basins of attraction, and the emergence of chaos through double routes via flip bifurcation and Neimark-Sacker bifurcations. We observe that increased adjustment speeds can destabilize the system, leading to a richness of dynamic complexity. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems—2nd Edition)
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16 pages, 2332 KB  
Article
Bayesian Approach to Stochastic Estimation of Population Survival Curves in Chile Using ABC Techniques and Its Impact over Social Structures
by Rolando Rubilar-Torrealba, Karime Chahuán-Jiménez, Hanns de la Fuente-Mella and Claudio Elórtegui-Gómez
Computation 2024, 12(8), 154; https://doi.org/10.3390/computation12080154 - 29 Jul 2024
Viewed by 2332
Abstract
In Chile and worldwide, life expectancy has consistently increased over the past six decades. Thus, the purpose of this study was to identify, measure, and estimate the population mortality ratios in Chile, mortality estimates are used to calculate life expectancy when constructing life [...] Read more.
In Chile and worldwide, life expectancy has consistently increased over the past six decades. Thus, the purpose of this study was to identify, measure, and estimate the population mortality ratios in Chile, mortality estimates are used to calculate life expectancy when constructing life tables. The Bayesian approach, specifically through Approximate Bayesian Computation (ABC) is employed to optimize parameter selection for these calculations. ABC corresponds to a class of computational methods rooted in Bayesian statistics that could be used to estimate the posterior distributions of the model parameters. For this research, ABC was applied to estimate the mortality ratios in Chile, using information available from 2004 to 2021. The results showed heterogeneity in the results when selecting the best model. Additionally, it was possible to generate projections for the next 10 years for the series analysed in the research. Finally, the main contribution of this research is that we measured and estimated the population mortality rates in Chile, defining the optimal selection of parameters, in order to contribute to creating a link between social and technical sciences for the advancement and implementation of current knowledge in the field of social structures. Full article
(This article belongs to the Special Issue Computational Social Science and Complex Systems—2nd Edition)
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