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Systems
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Data-Driven Decision Making for Complex Systems
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Data-Driven Decision Making for Complex Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Complex Systems and Cybernetics".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 6914

Special Issue Editors

Prof. Dr. Svajonė Bekešienė

E-Mail Website
Guest Editor
Research Group on Logistics and Defence Technology Management, Lithuanian Military Academy, Silo 5a, 10322 Vilnius, Lithuania
Interests: multi-purpose decision-making tasks; mathematical modeling; advances in theoretical mathematics and statistics (structural equation modeling); fuzzy logic; risk assessment and management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Šárka MAYEROVÁ

E-Mail Website
Guest Editor
Department of Mathematics and Physics, University of Defence, 66210 Brno, Czech Republic
Interests: fuzzy logic; soft computing; hypergroups; multicriteria; decision-making; risk assessment and management
Special Issues, Collections and Topics in MDPI journals
Dr. Marek Sedlačík

E-Mail Website
Guest Editor
Department of Quantitative Methods, University of Defence, 66210 Brno, Czech Republic
Interests: statistics; data mining; decision trees; multivariate classification methods

Special Issue Information

Dear Colleagues,

Data-driven decision making for complex systems is of paramount importance, particularly in the context of risk management within intricate systems such as financial markets, healthcare networks, and critical infrastructure. This Special Issue of Systems is a call to rekindle the original mission of system dynamics: gaining fresh insights into persistent security and risk-related challenges and crafting policies to address them. We invite you to submit approaches to models that showcase specific aspects of real-world systems, facilitating purposeful analysis of these prevalent issues.

Data-driven decision making is an indispensable tool for robust risk management in complex systems, where the sheer volume and complexity of data render traditional approaches less practical. It empowers organizations to proactively identify, assess, and mitigate risks, ultimately enhancing the resilience and reliability of complex systems.

This Special Issue is dedicated to establishing a scholarly foundation for data-driven decision making, with a specific focus on risk and education management within complex systems:

  • Risk Identification: Data analysis can uncover potential risks within complex systems by identifying patterns, anomalies, or factors that may lead to adverse events.
  • Risk Assessment: Data-driven models can quantify the likelihood and impact of identified risks, providing a structured way to prioritize and assess risks within the system.
  • Early Warning Systems: Complex systems can benefit from data-driven early warning systems that detect anomalies or deviations in real time, enabling proactive risk mitigation.
  • Scenario Analysis: Data analytics can be used to simulate various scenarios to assess the impact of potential risks, aiding in decision-making and risk response planning.
  • Optimization: Data-driven approaches can help optimize resource allocation and response strategies to minimize risks or their consequences.
  • Continuous Monitoring: Data-driven systems allow for ongoing monitoring of risks within complex systems, providing the capability to adapt and respond to evolving threats.
  • Education: Data-driven systems allow the enhancement of personal and systematic approaches to educational activities realization in light of modern technologies in the educational process.

Prof. Dr. Svajonė Bekešienė
Prof. Dr. Šárka MAYEROVÁ
Dr. Marek Sedlačík
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. Systems 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 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.

Prof. Dr. Svajonė Bekešienė
Prof. Dr. Šárka MAYEROVÁ
Dr. Marek Sedlačík
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. Systems 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 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.

Keywords

  • risk mitigation strategies
  • risk mitigation in healthcare networks
  • civil systems protection
  • natural systems management
  • cyber&ndash
  • physical systems
  • systems engineering
  • urban modeling and simulation
  • behavior modeling
  • resiliency
  • disaster management
  • pandemic management
  • data analytics
  • information management
  • decision making
  • artificial intelligence
  • machine learning
  • educational strategies

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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38 pages, 3147 KiB  
Article
A Risk-Optimized Framework for Data-Driven IPO Underperformance Prediction in Complex Financial Systems
by Mazin Alahmadi
Systems 2025, 13(3), 179; https://doi.org/10.3390/systems13030179 - 6 Mar 2025
Viewed by 730
Abstract
Accurate predictions of Initial Public Offerings (IPOs) aftermarket performance are essential for making informed investment decisions in the financial sector. This paper attempts to predict IPO short-term underperformance during a month post-listing. The current research landscape lacks modern models that address the needs [...] Read more.
Accurate predictions of Initial Public Offerings (IPOs) aftermarket performance are essential for making informed investment decisions in the financial sector. This paper attempts to predict IPO short-term underperformance during a month post-listing. The current research landscape lacks modern models that address the needs of small and imbalanced datasets relevant to emerging markets, as well as the risk preferences of investors. To fill this gap, we present a practical framework utilizing tree-based ensemble learning, including Bagging Classifier (BC), Random Forest (RF), AdaBoost (Ada), Gradient Boosting (GB), XGBoost (XG), Stacking Classifier (SC), and Extra Trees (ET), with Decision Tree (DT) as a base estimator. The framework leverages data-driven methodologies to optimize decision-making in complex financial systems, integrating ANOVA F-value for feature selection, Randomized Search for hyperparameter optimization, and SMOTE for class balance. The framework’s effectiveness is assessed using a hand-collected dataset that includes features from both pre-IPO prospectus and firm-specific financial data. We thoroughly evaluate the results using single-split evaluation and 10-fold cross-validation analysis. For the single-split validation, ET achieves the highest accuracy of 86%, while for the 10-fold validation, BC achieves the highest accuracy of 70%. Additionally, we compare the results of the proposed framework with deep-learning models such as MLP, TabNet, and ANN to assess their effectiveness in handling IPO underperformance predictions. These results demonstrate the framework’s capability to enable robust data-driven decision-making processes in complex and dynamic financial environments, even with limited and imbalanced datasets. The framework also proposes a dynamic methodology named Investor Preference Prediction Framework (IPPF) to match tree-based ensemble models to investors’ risk preferences when predicting IPO underperformance. It concludes that different models may be suitable for various risk profiles. For the dataset at hand, ET and Ada are more appropriate for risk-averse investors, while BC is suitable for risk-tolerant investors. The results underscore the framework’s importance in improving IPO underperformance predictions, which can better inform investment strategies and decision-making processes. Full article
(This article belongs to the Special Issue Data-Driven Decision Making for Complex Systems)
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15 pages, 2590 KiB  
Article
Improving Transfer Connectivity in Railway Timetables Based on Closeness Centrality: The Case of the European International Network
by Laura Calzada-Infante, Belarmino Adenso-Díaz and Santiago García Carbajal
Systems 2024, 12(9), 327; https://doi.org/10.3390/systems12090327 - 26 Aug 2024
Viewed by 1280
Abstract
Could the connectivity of a global railway network increase through small changes in the timetable services? When designing railway schedules, transfer connections to intermediate stations may not be the primary focus considered. However, they may have an important influence on connectivity. In this [...] Read more.
Could the connectivity of a global railway network increase through small changes in the timetable services? When designing railway schedules, transfer connections to intermediate stations may not be the primary focus considered. However, they may have an important influence on connectivity. In this paper, we study the potential improvement in connections by introducing small changes to the current schedules, using real timetables from all international railway services in Europe. The modelling was completed using the Complex Networks methodology and performance was measured based on total closeness centrality. Various factors are considered to calibrate the necessary amendments to provide a better traveller service, including connection times at stations and different allowed levels of schedule changes. The results indicate that by changing the schedule of only 1% of the services by at most 10 min, the connectivity improvement is remarkable. Railway companies should consider this result in order to expand the potential use of the international railway service at a time when public transportation must be encouraged. Full article
(This article belongs to the Special Issue Data-Driven Decision Making for Complex Systems)
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26 pages, 3002 KiB  
Article
Evaluating the Reputation of Internet Financial Platforms in China: A Sustainable Operations Perspective
by Ge You, Hao Guo, Abd Alwahed Dagestani and Ibrahim Alnafrah
Systems 2024, 12(8), 279; https://doi.org/10.3390/systems12080279 - 1 Aug 2024
Viewed by 1561
Abstract
In China, many Internet financial platforms (IFPs) are grappling with sustainability challenges due to elevated default rates, which have triggered widespread investor anxiety. To evaluate the sustainability practices of these platforms, we propose a reputation evaluation model designed to rank IFPs based on [...] Read more.
In China, many Internet financial platforms (IFPs) are grappling with sustainability challenges due to elevated default rates, which have triggered widespread investor anxiety. To evaluate the sustainability practices of these platforms, we propose a reputation evaluation model designed to rank IFPs based on their sustainability. The economic sustainability of an IFP is decomposed into three components: scale strength, capital liquidity, and sustainable operating capability. Through an analysis of the correlation relationships between various indicators, we have identified nine significant indicators. Mathematical models are established to quantify these nine indicator variables. Subsequently, the score values of each indicator are integrated to establish a reputation evaluation model utilizing the weighted geometric mean method. Furthermore, the reputation evaluation values for 18 Chinese IFPs were calculated using the developed model, and the sustainability of the platforms was ranked according to the reputation evaluation value. A comparative analysis was also conducted between the sustainable rankings proposed in this study and the development rankings of the “Home of Online Loans” (HOL). The results reveal that our model effectively considers both the current operational strength and the sustainable development capability of the platform. It successfully identifies platforms with poor sustainability, assisting investors in making more informed decisions. Simultaneously, this study identifies key indicators influencing the sustainability of IFPs, providing valuable insights for managers seeking to enhance the sustainable operational levels of their platforms. Full article
(This article belongs to the Special Issue Data-Driven Decision Making for Complex Systems)
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Review

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43 pages, 729 KiB  
Review
A Systematic Survey of Distributed Decision Support Systems in Healthcare
by Basem Almadani, Hunain Kaisar, Irfan Rashid Thoker and Farouq Aliyu
Systems 2025, 13(3), 157; https://doi.org/10.3390/systems13030157 - 26 Feb 2025
Viewed by 1084
Abstract
The global Internet of Medical Things (IoMT) market is growing at a Compound Annual Growth Rate (CAGR) of 17.8%, a testament to the increasing demand for IoMT in the health sector. However, more IoMT devices mean an increase in the volume and velocity [...] Read more.
The global Internet of Medical Things (IoMT) market is growing at a Compound Annual Growth Rate (CAGR) of 17.8%, a testament to the increasing demand for IoMT in the health sector. However, more IoMT devices mean an increase in the volume and velocity of data received by healthcare decision-makers, leading many to develop Distributed Decision Support Systems (DDSSs) to help them make accurate and timely decisions. This research is a systematic review of DDSSs in healthcare using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The study explores how advanced technologies such as Artificial Intelligence (AI), IoMT, and blockchain enhance clinical decision-making processes. It highlights key innovations in DDSSs, including hybrid imaging techniques for comprehensive disease characterization. It also examines the role of Case-Based Reasoning (CBR) frameworks in improving personalized treatment strategies for chronic diseases like diabetes mellitus. It also presents challenges of applying DDSSs in the healthcare sector, such as security and privacy, system integration, and interoperability issues. Finally, it discusses open issues as future research directions in the field of DDSSs in the healthcare sector, including data structure standardization, alert fatigue for healthcare workers using DDSSs, and the lack of adherence of emerging technologies like blockchain to medical regulations. Full article
(This article belongs to the Special Issue Data-Driven Decision Making for Complex Systems)
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17 pages, 1197 KiB  
Review
Resilience Metrics for Socio-Ecological and Socio-Technical Systems: A Scoping Review
by Patrick Steinmann, Hilde Tobi and George A. K. van Voorn
Systems 2024, 12(9), 357; https://doi.org/10.3390/systems12090357 - 10 Sep 2024
Viewed by 1270
Abstract
An increased interest in the resilience of complex socio-ecological and socio-technical systems has led to a variety of metrics being proposed. An overview of these metrics and their underlying concepts would support identifying useful metrics for applications in science and engineering. This study [...] Read more.
An increased interest in the resilience of complex socio-ecological and socio-technical systems has led to a variety of metrics being proposed. An overview of these metrics and their underlying concepts would support identifying useful metrics for applications in science and engineering. This study undertakes a scoping review of resilience metrics for systems straddling the societal, ecological, and technical domains to determine how resilience has been measured, the conceptual differences between the proposed approaches, and how they align with the domains of their case studies. We find that a wide variety of resilience metrics have been proposed in the literature. Conceptually, ten different quantification approaches were identified. Four different disturbance types were observed, including sudden, continuous, multiple, and abruptly ending disturbances. Surprisingly, there is no strong pattern regarding socio-ecological systems being studied using the “ecological resilience” concept and socio-technical systems being studied using the “engineering resilience” concept. As a result, we recommend that researchers use multiple resilience metrics in the same study, ideally following different conceptual approaches, and compare the resulting insights. Furthermore, the used metrics should be mathematically defined, the included variables explained and their units provided, and the chosen functional form justified. Full article
(This article belongs to the Special Issue Data-Driven Decision Making for Complex Systems)
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