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Applied Sciences
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Advances in Complexity Science through Modeling and Simulation
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Advances in Complexity Science through Modeling and Simulation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 16371

Special Issue Editors

Prof. Dr. Vittorio Scarano

E-Mail Website
Guest Editor
Department of Computer Science, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
Interests: distributed systems; computer graphics; virtual and augmented reality; computer-supported collaborative work; open data
Prof. Dr. Gennaro Cordasco

E-Mail Website
Guest Editor
Department of Psychology, Università degli Studi della Campania "Luigi Vanvitelli", Viale Abramo Lincoln, 5, 81100 Caserta, CE, Italy
Interests: distributed algorithms; peer-to-peer (P2P) systems; small-world networks; Internet-based computing
Dr. Carmine Spagnuolo

E-Mail Website
Guest Editor
Department of Computer Science, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
Interests: distributed algorithms; HPC; parallel and distributed agent-based simulations; networks

Special Issue Information

Dear Colleagues,

A wide range of natural, social, and artificial organizations are characterized by many strongly interacting components, which give life to behaviors that are incomprehensible upon examining a single piece. Such organizations are commonly referred to as complex systems, and as examples we can name traffic control, weather, policy making, and epidemic dynamics. Complexity science is a broad umbrella term that embraces this aspect and several disciplines, each with its tools, languages, and specific methods, to formulate a model that can imitate real-world systems. Under this paradigm, complex systems are dealt with using different approaches. For the purpose of this Special Issue, we are interested in theoretical and practical research results related to the analysis of complexity via agent-based simulations, network science, and artificial intelligence under the lens of computational frameworks, algorithms, and software tools.

Agent-based simulations are a widely used analysis tool in this area since they make it possible to cope with complexity via a bottom-up approach by defining the behaviors of independent and interacting entities (agents). Such entities have a specified set of characteristics and interact with each other and their environment according to predefined rules. Agents may represent individuals, viruses, governments, or any other entities of interest. Further, they may adapt their behavior to their experiences, interactions with other agents, and interactions with their environment. In this context, network science comes into play as agents’ interaction patterns are usually described with a network, commonly via the mathematical structure of graphs, which permits modeling pairwise relations, or, more recently, with hypergraphs, which capture many-to-many interactions. Finally, research on complex systems is empowered by using artificial intelligence to model complex autonomous agent behaviors and analyze emergent system properties.

The final destination of complex systems research is to provide more accurate and efficient models to better understand and imitate real-world systems at different scales. However, researchers have to face myriad challenges from the computational perspective, such as supporting multi-disciplinary and cooperative design, hyperparameter optimization, presentation of findings, visualization, integrating real-time data, and handling increasingly computation-intensive models and analysis.

The purpose of this Special Issue is to present a collection of the latest research in the broad field of complexity science, with a specific focus on software frameworks and modeling tools for the analysis of complex systems. Articles devoted to advances in agent-based simulation, network-based models, AI integration, and enabling large-scale analysis using parallel and distributed computation are within the scope of this Special Issue. We welcome the submission of research results from academia or industry, either theoretical or practical, as well as scalable applications and review articles.

Topics:

  • Agent-based simulation, network science, and artificial intelligence applied to complexity science.
  • Formal models for large-scale complex systems.
  • Large-scale systems tools and frameworks
    • Parallel computing;
    • Distributed computing;
    • Cloud computing, multi-cloud, edge computing.
  • Visualization methodologies and software tools.
  • Application of complexity science.
  • Review articles about complexity science.

Prof. Dr. Vittorio Scarano
Prof. Dr. Gennaro Cordasco
Dr. Carmine Spagnuolo
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

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Published Papers (4 papers)

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Research

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21 pages, 7512 KiB  
Article
A Complex Network Important Node Identification Based on the KPDN Method
by Liang Zhao, Peng Sun, Jieyong Zhang, Miao Peng, Yun Zhong and Wei Liang
Appl. Sci. 2023, 13(14), 8303; https://doi.org/10.3390/app13148303 - 18 Jul 2023
Cited by 6 | Viewed by 1789
Abstract
In complex networks, identifying influential nodes is of great significance for their wide application. The proposed method integrates the correlation properties of local and global, and in terms of global features, the K-shell decomposition method of fusion degree is used to improve the [...] Read more.
In complex networks, identifying influential nodes is of great significance for their wide application. The proposed method integrates the correlation properties of local and global, and in terms of global features, the K-shell decomposition method of fusion degree is used to improve the actual discrimination degree of each node. In terms of local characteristics, the Solton index is introduced to effectively show the association relationship between each node and adjacent nodes. Through the analysis and comparison of multiple existing methods, it is found that the proposed method can identify key nodes more accurately so as to help quickly disintegrate the network. The final manual network verification also shows that this method is also suitable for the identification of important nodes of small-world networks and community networks. Full article
(This article belongs to the Special Issue Advances in Complexity Science through Modeling and Simulation)
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20 pages, 2411 KiB  
Article
Multilayered Emergent Phenomena Caused by Basic Income and Labor Supply on the Wider Economic System
by Kosei Takashima and Isao Yagi
Appl. Sci. 2023, 13(13), 7588; https://doi.org/10.3390/app13137588 - 27 Jun 2023
Cited by 1 | Viewed by 1326
Abstract
Despite the growing interest in basic income (BI) in recent years, the existing research has mainly focused on its impact on household finances. However, changes in household behavior may affect the actions of other decision makers, such as businesses and governments, leading to [...] Read more.
Despite the growing interest in basic income (BI) in recent years, the existing research has mainly focused on its impact on household finances. However, changes in household behavior may affect the actions of other decision makers, such as businesses and governments, leading to unanticipated outcomes. Therefore, any analysis of BI must use a model with multilayered feedback from the actions of individual decision makers. To actualize such a model, household budgets, firms, and other entities must autonomously determine production levels, prices, and other factors, thereby encompassing a complete circulation of funds. This study constructs a macroeconomic model using agent-based modeling as a basic framework to achieve these goals, and it analyzes the emergent behaviors generated by BI and the labor supply in the economic system. The results show that although BI brings about more equitable consumption by households, it also creates a unique phenomenon wherein Gross Domestic Product increases but economic activity in terms of capital investment stagnates. Upon examining the impact of BI, the results of this study present the need to examine the multilayered feedback influencing mutual decision makers, which arises from the behavioral changes of individual decision makers caused by BI. Full article
(This article belongs to the Special Issue Advances in Complexity Science through Modeling and Simulation)
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22 pages, 2773 KiB  
Article
An Agent-Based Simulation of How Promotion Biases Impact Corporate Gender Diversity
by Chibin Zhang and Paolo Gaudiano
Appl. Sci. 2023, 13(4), 2457; https://doi.org/10.3390/app13042457 - 14 Feb 2023
Cited by 1 | Viewed by 2963
Abstract
Diversity and inclusion (D&I) is a topic of increasing relevance across virtually all sectors of our society, with the potential for a significant impact on corporations and more broadly on our economy and society. While people are typically the most valuable asset of [...] Read more.
Diversity and inclusion (D&I) is a topic of increasing relevance across virtually all sectors of our society, with the potential for a significant impact on corporations and more broadly on our economy and society. While people are typically the most valuable asset of every organization, human resources (HR) in general, and D&I in particular, are dominated by qualitative approaches. This paper introduces an agent-based simulation that can quantify the impact of certain aspects of D&I on corporate performance. The simulation provides a parsimonious and compelling explanation of the impact of hiring and promotion biases on the resulting corporate gender balance, accurately replicating real-world data about gender imbalances across multiple industry sectors. In addition, the paper shows that the simulation can be used to predict the likely impact of different D&I interventions. Specifically, once a company has become imbalanced, even removing all promotion biases is not sufficient to rectify the situation, and it can take decades to undo the imbalances initially created by these biases. These and other results demonstrate that agent-based simulation is a powerful approach for managing D&I in corporate settings and could become an invaluable tool for the strategic and tactical management of human resources. Full article
(This article belongs to the Special Issue Advances in Complexity Science through Modeling and Simulation)
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Review

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22 pages, 433 KiB  
Review
Experimenting with Agent-Based Model Simulation Tools
by Alessia Antelmi, Gennaro Cordasco, Giuseppe D’Ambrosio, Daniele De Vinco and Carmine Spagnuolo
Appl. Sci. 2023, 13(1), 13; https://doi.org/10.3390/app13010013 - 20 Dec 2022
Cited by 19 | Viewed by 9251
Abstract
Agent-based models (ABMs) are one of the most effective and successful methods for analyzing real-world complex systems by investigating how modeling interactions on the individual level (i.e., micro-level) leads to the understanding of emergent phenomena on the system level (i.e., macro-level). ABMs represent [...] Read more.
Agent-based models (ABMs) are one of the most effective and successful methods for analyzing real-world complex systems by investigating how modeling interactions on the individual level (i.e., micro-level) leads to the understanding of emergent phenomena on the system level (i.e., macro-level). ABMs represent an interdisciplinary approach to examining complex systems, and the heterogeneous background of ABM users demands comprehensive, easy-to-use, and efficient environments to develop ABM simulations. Currently, many tools, frameworks, and libraries exist, each with its characteristics and objectives. This article aims to guide newcomers in the jungle of ABM tools toward choosing the right tool for their skills and needs. This work proposes a thorough overview of open-source general-purpose ABM tools and offers a comparison from a two-fold perspective. We first describe an off-the-shelf evaluation by considering each ABM tool’s features, ease of use, and efficiency according to its authors. Then, we provide a hands-on evaluation of some ABM tools by judging the effort required in developing and running four ABM models and the obtained performance. Full article
(This article belongs to the Special Issue Advances in Complexity Science through Modeling and Simulation)
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