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Social Sciences

A topical collection in Entropy (ISSN 1099-4300). This collection belongs to the section "Multidisciplinary Applications".

Viewed by 16323

Editor

Prof. Dr. Miguel A. Fuentes

E-Mail Website
Collection Editor
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
Interests: complexity science; nonlinear phenomena; stochastic calculus; Kolmogorov complexity; complexity measures
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

Entropy is one of the most important concepts arising in the evolution of physics and information theory. During his conceptual and theoretical development, it has greatly influenced different fields of science, helping to gain a better understanding of macroscopic and universal phenomena, among others.

During the last several decades, a significant number of applications showing the novel use of entropy or information-theoretic concepts have been applied to social sciences. This increasing interdisciplinary effort is obtaining significant new results with impacts in different social systems arenas, like economics, public policy, epidemic control, energy use, etc.

This collection aims to provide a specific meeting point between concepts, methods, and applications coming from entropy theory and social sciences. It is open to original research and review articles on specific social science topics of interest, which include (but are not limited to):

  • Network theory;
  • Nonlinear dynamics;
  • Statistical mechanics;
  • Game theory;
  • Big data;
  • Maximum entropy methods;
  • Shannon (and other) entropy functions;
  • Self-organization;
  • Simplicity and complexity;
  • Social networking;
  • Artificial intelligence;
  • Neural networks;
  • Cybernetics;
  • Robotics;
  • Human–machine interfaces;
  • Info-metrics.

Prof. Dr. Miguel A. Fuentes
Collection 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 collection 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

  • Social science
  • Complexity theory
  • Network theory
  • Nonlinear dynamics
  • Game theory
  • Statistical mechanics
  • Big data
  • AI
  • Human–machine interface

Published Papers (5 papers)

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2021

Jump to: 2020

15 pages, 272 KiB  
Article
Analysis on International Competitiveness of Service Trade in the Guangdong–Hong Kong–Macao Greater Bay Area Based on Using the Entropy and Gray Correlation Methods
by Xianhang Xu, Mohd Anuar Arshad and Arshad Mahmood
Entropy 2021, 23(10), 1253; https://doi.org/10.3390/e23101253 - 26 Sep 2021
Cited by 5 | Viewed by 3221
Abstract
Based on the analysis and measurement of the overall situation, import and export structure and international competitiveness of the various sectors of service trade in the Guangdong–Hong Kong–Macao Greater Bay Area, with the help of MATLAB and Gray System Modeling software, the synergy [...] Read more.
Based on the analysis and measurement of the overall situation, import and export structure and international competitiveness of the various sectors of service trade in the Guangdong–Hong Kong–Macao Greater Bay Area, with the help of MATLAB and Gray System Modeling software, the synergy degree model was established to quantitatively analyze the synergy level of service trade in the Greater Bay Area with the help of grey correlation analysis method and entropy weight method. The results show that the overall development trend of service trade in the Guangdong–Hong Kong–Macao Greater Bay Area is good. The service trade industries in different regions are highly complementary and have a high degree of correlation. The potential for the coordinated development of internal service trade is excellent, and the overall situation of service trade in the Greater Bay Area is in a stage of transition from a moderate level of synergy to a high level of synergy. The Greater Bay Area can achieve industrial synergy by accelerating industrial integration and green transformation, establishing a coordinated development mechanism, sharing market platform, strengthening personnel security, and further enhancing the international competitiveness of service trade. The established model better reflects the current coordination of service trade in the Guangdong–Hong Kong–Macao Greater Bay Area and has good applicability. In the future, more economic, technological, geographic, and policy data and information can be comprehensively used to study the spatial pattern, evolution rules, and mechanisms of coordinated development in the broader area. Full article
12 pages, 261 KiB  
Article
How to Measure a Two-Sided Market
by Wen Zheng and Xiaoming Yao
Entropy 2021, 23(8), 962; https://doi.org/10.3390/e23080962 - 27 Jul 2021
Cited by 2 | Viewed by 1797
Abstract
Applying the theories of complex network and entropy measurement to the market, the two-sided market structure is analyzed in constructing the O2O platform transaction on the entropy measurement of the nodes and links. Market structure entropy (MSE) is initially introduced to measure the [...] Read more.
Applying the theories of complex network and entropy measurement to the market, the two-sided market structure is analyzed in constructing the O2O platform transaction on the entropy measurement of the nodes and links. Market structure entropy (MSE) is initially introduced to measure the consistency degree of the individuals and the groups in the O2O market, according to the interaction in the profits, the time/space, and the information relationship. Considering that the market structure entropies are changing upward or downward, MSE is used to judge the consistency degree between the individuals and the groups. Respectively, considering the scale, the cost and the value dimensions, MSE is expanded to explain the market quality entropy, the market time-effect entropy, and the market capacity entropy.MSE provides a methodology in studying the O2O platform transaction and gives the quantitative index in the evaluation of the O2O market state. Full article
13 pages, 1952 KiB  
Article
Unifying Node Labels, Features, and Distances for Deep Network Completion
by Qiang Wei and Guangmin Hu
Entropy 2021, 23(6), 771; https://doi.org/10.3390/e23060771 - 18 Jun 2021
Cited by 3 | Viewed by 2415
Abstract
Collected network data are often incomplete, with both missing nodes and missing edges. Thus, network completion that infers the unobserved part of the network is essential for downstream tasks. Despite the emerging literature related to network recovery, the potential information has not been [...] Read more.
Collected network data are often incomplete, with both missing nodes and missing edges. Thus, network completion that infers the unobserved part of the network is essential for downstream tasks. Despite the emerging literature related to network recovery, the potential information has not been effectively exploited. In this paper, we propose a novel unified deep graph convolutional network that infers missing edges by leveraging node labels, features, and distances. Specifically, we first construct an estimated network topology for the unobserved part using node labels, then jointly refine the network topology and learn the edge likelihood with node labels, node features and distances. Extensive experiments using several real-world datasets show the superiority of our method compared with the state-of-the-art approaches. Full article
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28 pages, 6500 KiB  
Article
Increased Neural Efficiency in Visual Word Recognition: Evidence from Alterations in Event-Related Potentials and Multiscale Entropy
by Kelsey Cnudde, Sophia van Hees, Sage Brown, Gwen van der Wijk, Penny M. Pexman and Andrea B. Protzner
Entropy 2021, 23(3), 304; https://doi.org/10.3390/e23030304 - 4 Mar 2021
Cited by 5 | Viewed by 3386
Abstract
Visual word recognition is a relatively effortless process, but recent research suggests the system involved is malleable, with evidence of increases in behavioural efficiency after prolonged lexical decision task (LDT) performance. However, the extent of neural changes has yet to be characterized in [...] Read more.
Visual word recognition is a relatively effortless process, but recent research suggests the system involved is malleable, with evidence of increases in behavioural efficiency after prolonged lexical decision task (LDT) performance. However, the extent of neural changes has yet to be characterized in this context. The neural changes that occur could be related to a shift from initially effortful performance that is supported by control-related processing, to efficient task performance that is supported by domain-specific processing. To investigate this, we replicated the British Lexicon Project, and had participants complete 16 h of LDT over several days. We recorded electroencephalography (EEG) at three intervals to track neural change during LDT performance and assessed event-related potentials and brain signal complexity. We found that response times decreased during LDT performance, and there was evidence of neural change through N170, P200, N400, and late positive component (LPC) amplitudes across the EEG sessions, which suggested a shift from control-related to domain-specific processing. We also found widespread complexity decreases alongside localized increases, suggesting that processing became more efficient with specific increases in processing flexibility. Together, these findings suggest that neural processing becomes more efficient and optimized to support prolonged LDT performance. Full article
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2020

Jump to: 2021

32 pages, 17597 KiB  
Article
A Bayesian Entropy Approach to Sectoral Systemic Risk Modeling
by Radu Lupu, Adrian Cantemir Călin, Cristina Georgiana Zeldea and Iulia Lupu
Entropy 2020, 22(12), 1371; https://doi.org/10.3390/e22121371 - 4 Dec 2020
Cited by 8 | Viewed by 2968
Abstract
We investigate the dynamics of systemic risk of European companies using an approach that merges paradigmatic risk measures such as Marginal Expected Shortfall, CoVaR, and Delta CoVaR, with a Bayesian entropy estimation method. Our purpose is to bring to light potential spillover effects [...] Read more.
We investigate the dynamics of systemic risk of European companies using an approach that merges paradigmatic risk measures such as Marginal Expected Shortfall, CoVaR, and Delta CoVaR, with a Bayesian entropy estimation method. Our purpose is to bring to light potential spillover effects of the entropy indicator for the systemic risk measures computed on the 24 sectors that compose the STOXX 600 index. Our results show that several sectors have a high proclivity for generating spillovers. In general, the largest influences are delivered by Capital Goods, Banks, Diversified Financials, Insurance, and Real Estate. We also bring detailed evidence on the sectors that are the most pregnable to spillovers and on those that represent the main contributors of spillovers. Full article
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