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Complex Systems Approach to Social Dynamics
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Complex Systems Approach to Social Dynamics

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 6456

Special Issue Editors

Dr. Yerali Gandica

E-Mail Website
Guest Editor
CNRS, Laboratoire de Physique Théorique et Modelisation, Department of Physics, Université de Cergy-Pontoise, 95000 Paris, France
Interests: complex systems; network science; computational social sciences
Dr. Floriana Gargiulo

E-Mail Website
Guest Editor
CNRS, Université Paris-Sorbonne, 75017 Paris, France
Interests: computational social science; network science; complex systems

Special Issue Information

Dear Colleagues,

Complex System science aims to study the phenomena that emerge as a consequence of the interactions between the constituents and, thus, cannot be understood by studying a singular, isolated component. The field has incorporated concepts and methods deriving from many areas, ranging from statistical physics and biology to economics and sociology, which, in a constant process of cross-fertilization, have given rise to new types of questions framed into the field of Complex Systems.

The study of interacting particle systems has, for a long time, been an essential subject of physics. The use of statistical methods has allowed for significant advances in this area by providing a bridge between the microscopic interactions and the large collective behaviour of the system. This success has motivated researchers to try a statistical approach to other subjects outside of physics.

One of the complex interactions studied by this field of knowledge is human interactions. The application of the methods of statistical physics to social phenomena, where the interacting particles are now interacting human beings, has proven to be very fruitful in allowing for the understanding of many features of human behaviour.

This Special Issue aims to collect scientific research using the tools from the field of complex systems to address social phenomena, for example:

- Social-related, agent-based models;

- The role of complex topologies;

- Network science;

- Opinion dynamics;

- Semantic networks;

- Computational Social Sciences;

- Scientific collaboration and citation dynamics;

- Digital controversies;

- Gender issues.

Dr. Yerali Gandica
Dr. Floriana Gargiulo
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • complex systems
  • network science
  • opinion dynamics
  • agent-based models
  • digital footprints
  • semantic networks
  • gender imbalance

Published Papers (5 papers)

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Research

19 pages, 556 KiB  
Article
A New Forecasting Approach for Oil Price Using the Recursive Decomposition–Reconstruction–Ensemble Method with Complexity Traits
by Fang Wang, Menggang Li and Ruopeng Wang
Entropy 2023, 25(7), 1051; https://0-doi-org.brum.beds.ac.uk/10.3390/e25071051 - 12 Jul 2023
Viewed by 902
Abstract
The subject of oil price forecasting has obtained an incredible amount of interest from academics and policymakers in recent years due to the widespread impact that it has on various economic fields and markets. Thus, a novel method based on decomposition–reconstruction–ensemble for crude [...] Read more.
The subject of oil price forecasting has obtained an incredible amount of interest from academics and policymakers in recent years due to the widespread impact that it has on various economic fields and markets. Thus, a novel method based on decomposition–reconstruction–ensemble for crude oil price forecasting is proposed. Based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, in this paper we construct a recursive CEEMDAN decomposition–reconstruction–ensemble model considering the complexity traits of crude oil data. In this model, the steps of mode reconstruction, component prediction, and ensemble prediction are driven by complexity traits. For illustration and verification purposes, the West Texas Intermediate (WTI) and Brent crude oil spot prices are used as the sample data. The empirical result demonstrates that the proposed model has better prediction performance than the benchmark models. Thus, the proposed recursive CEEMDAN decomposition–reconstruction–ensemble model can be an effective tool to forecast oil price in the future. Full article
(This article belongs to the Special Issue Complex Systems Approach to Social Dynamics)
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13 pages, 589 KiB  
Article
Popularity and Entropy in Friendship and Enmity Networks in Classrooms
by Diego B. Sánchez-Espinosa, Eric Hernández-Ramírez and Marcelo del Castillo-Mussot
Entropy 2023, 25(7), 971; https://0-doi-org.brum.beds.ac.uk/10.3390/e25070971 - 23 Jun 2023
Cited by 2 | Viewed by 888
Abstract
Looking for regular statistical trends of relations in schools, we constructed 42 independent weighted directed networks of simultaneous friendship and animosity from surveys we made in the Mexico City Metropolitan area in classrooms with students of different ages and levels by asking them [...] Read more.
Looking for regular statistical trends of relations in schools, we constructed 42 independent weighted directed networks of simultaneous friendship and animosity from surveys we made in the Mexico City Metropolitan area in classrooms with students of different ages and levels by asking them to nominate and order five friends and five foes. However, the data show that older students nominated fewer than the five required five foes. Although each classroom was independent of the others, we found several general trends involving students of different ages and grade levels. In all classrooms, friendship entropy was found to be higher than enmity entropy, indicating that fewer students received enmity links than received friendship nominations. Popular agents exhibited more reciprocal nominations among themselves than less popular agents, and opposite-sex friendships increased with age. Full article
(This article belongs to the Special Issue Complex Systems Approach to Social Dynamics)
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14 pages, 2972 KiB  
Article
Artificial Intelligence Techniques Used to Extract Relevant Information from Complex Social Networks
by Santiago Paramés-Estévez, Alejandro Carballosa, David Garcia-Selfa and Alberto P. Munuzuri
Entropy 2023, 25(3), 507; https://0-doi-org.brum.beds.ac.uk/10.3390/e25030507 - 16 Mar 2023
Cited by 1 | Viewed by 1403
Abstract
Social networks constitute an almost endless source of social behavior information. In fact, sometimes the amount of information is so large that the task to extract meaningful information becomes impossible due to temporal constrictions. We developed an artificial-intelligence-based method that reduces the calculation [...] Read more.
Social networks constitute an almost endless source of social behavior information. In fact, sometimes the amount of information is so large that the task to extract meaningful information becomes impossible due to temporal constrictions. We developed an artificial-intelligence-based method that reduces the calculation time several orders of magnitude when conveniently trained. We exemplify the problem by extracting data freely available in a commonly used social network, Twitter, building up a complex network that describes the online activity patterns of society. These networks are composed of a huge number of nodes and an even larger number of connections, making extremely difficult to extract meaningful data that summarizes and/or describes behaviors. Each network is then rendered into an image and later analyzed using an AI method based on Convolutional Neural Networks to extract the structural information. Full article
(This article belongs to the Special Issue Complex Systems Approach to Social Dynamics)
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16 pages, 2101 KiB  
Article
Robustness Evaluation of the Open Source Product Community Network Considering Different Influential Nodes
by Hongli Zhou, Siqing You and Mingxuan Yang
Entropy 2022, 24(10), 1355; https://0-doi-org.brum.beds.ac.uk/10.3390/e24101355 - 24 Sep 2022
Cited by 1 | Viewed by 994
Abstract
With the rapid development of Internet technology, the innovative value and importance of the open source product community (OSPC) is becoming increasingly significant. Ensuring high robustness is essential to the stable development of OSPC with open characteristics. In robustness analysis, degree and betweenness [...] Read more.
With the rapid development of Internet technology, the innovative value and importance of the open source product community (OSPC) is becoming increasingly significant. Ensuring high robustness is essential to the stable development of OSPC with open characteristics. In robustness analysis, degree and betweenness are traditionally used to evaluate the importance of nodes. However, these two indexes are disabled to comprehensively evaluate the influential nodes in the community network. Furthermore, influential users have many followers. The effect of irrational following behavior on network robustness is also worth investigating. To solve these problems, we built a typical OSPC network using a complex network modeling method, analyzed its structural characteristics and proposed an improved method to identify influential nodes by integrating the network topology characteristics indexes. We then proposed a model containing a variety of relevant node loss strategies to simulate the changes in robustness of the OSPC network. The results showed that the proposed method can better distinguish the influential nodes in the network. Furthermore, the network’s robustness will be greatly damaged under the node loss strategies considering the influential node loss (i.e., structural hole node loss and opinion leader node loss), and the following effect can greatly change the network robustness. The results verified the feasibility and effectiveness of the proposed robustness analysis model and indexes. Full article
(This article belongs to the Special Issue Complex Systems Approach to Social Dynamics)
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7 pages, 268 KiB  
Article
Coevolutionary Dynamics with Global Fields
by Mario G. Cosenza and José L. Herrera-Diestra
Entropy 2022, 24(9), 1239; https://0-doi-org.brum.beds.ac.uk/10.3390/e24091239 - 03 Sep 2022
Viewed by 1280
Abstract
We investigate the effects of external and autonomous global interaction fields on an adaptive network of social agents with an opinion formation dynamics based on a simple imitation rule. We study the competition between global fields and adaptive rewiring on the space of [...] Read more.
We investigate the effects of external and autonomous global interaction fields on an adaptive network of social agents with an opinion formation dynamics based on a simple imitation rule. We study the competition between global fields and adaptive rewiring on the space of parameters of the system. The model represents an adaptive society subject to global mass media such as a directed opinion influence or feedback of endogenous cultural trends. We show that, in both situations, global mass media contribute to consensus and to prevent the fragmentation of the social network induced by the coevolutionary dynamics. We present a discussion of these results in the context of dynamical systems and opinion formation dynamics. Full article
(This article belongs to the Special Issue Complex Systems Approach to Social Dynamics)
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