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Data-Driven Decision-Making in Intelligent Systems
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Data-Driven Decision-Making in Intelligent Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 4574

Special Issue Editor

Dr. Alexandros Bousdekis

E-Mail Website
Guest Editor
Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), 9 Iroon Polytechniou, Zografou, 15780 Athens, Greece
Interests: industry 4.0; intelligent systems; management of information systems; predictive and prescriptive analytics; real-time decision-making; proactive and event-driven computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data science encompasses the principles, processes, and methods used to analyze data in order to derive useful insights. In this way, data analytics is capable of enhancing decision-making in the business context, either referring to long-term management decisions or to (near) real-time decisions, usually at the operational level. The literature, but also real-life applications, demonstrate that data-driven decision-making exhibits higher productivity and efficiency, as well as higher returns on assets, equity, and market value. Therefore, there is a wide range of actual and potential applications, such as direct marketing, online advertising, credit scoring, financial trading, fraud detection, search ranking, telecommunications, product recommendations, manufacturing operations, etc. With the emergence of intelligent systems, more and more information systems have been developed in order to automate data-driven decision-making. However, information systems development has to face new challenges derived from the large amounts of data, from heterogeneous data sources, existing in the business environments. This Special Issue welcomes research works dealing with the design and development of intelligent systems for data-driven decision-making. In this sense, it welcomes research works on architectures, algorithms, methods, and application-oriented research on exploiting data for supporting decision-making with the use of information systems.

Dr. Alexandros Bousdekis
Guest 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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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

  • intelligent systems
  • data analytics
  • machine learning
  • decision-making
  • information systems for data-driven decision-making

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Published Papers (4 papers)

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21 pages, 1187 KB  
Article
RW-UCFI: A Risk-Weighted Uncertainty-Conditioned Explainability Framework for Stacked Ensemble Models in B2B Financial Risk Profiling
by Carolus Borromeus Widiyatmoko, Rahmat Gernowo and Budi Warsito
Information 2026, 17(4), 363; https://doi.org/10.3390/info17040363 - 10 Apr 2026
Viewed by 206
Abstract
Interpretability in corporate financial risk profiling must support not only predictive performance but also governance-oriented decision-making. This study proposes a three-class financial risk assessment workflow for B2B settings and introduces Risk-Weighted Uncertainty-Conditioned Feature Importance (RW-UCFI) as a post-explanation prioritization framework. RW-UCFI is not [...] Read more.
Interpretability in corporate financial risk profiling must support not only predictive performance but also governance-oriented decision-making. This study proposes a three-class financial risk assessment workflow for B2B settings and introduces Risk-Weighted Uncertainty-Conditioned Feature Importance (RW-UCFI) as a post-explanation prioritization framework. RW-UCFI is not a new attribution method; rather, it reorganizes existing explanation outputs according to class sensitivity, predictive uncertainty, and asymmetric risk relevance. The empirical analysis uses a single cross-sectional dataset of 954 Indonesia Stock Exchange-listed firms with organizationally provided Low Risk, Medium Risk, and High Risk labels. A stacked ensemble model is used as the explanatory substrate, followed by calibration analysis, uncertainty analysis, and governance-oriented explainability aggregation. On the held-out validation set, the model achieved an accuracy of 0.7487 and a macro ROC-AUC of 0.8630. Repeated stratified validation indicated moderately stable aggregate performance, although class-level reliability remained uneven, with High Risk recall emerging as the weakest and most variable component. The original model showed the most favorable probability reliability among the evaluated variants, whereas temperature scaling and one-vs-rest isotonic regression did not improve calibration. Uncertainty analysis further showed that the most uncertain cases concentrated substantially more misclassifications and High Risk misses; the top 30% most uncertain cases captured 52.1% of all errors and 43.8% of High Risk misses. RW-UCFI produced a materially different feature-priority structure from standard global SHAP ranking, suggesting that explanation outputs may become more decision-relevant for governance-oriented review when contextualized by uncertainty and asymmetric risk conditions in the present setting. Full article
(This article belongs to the Special Issue Data-Driven Decision-Making in Intelligent Systems)
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22 pages, 2714 KB  
Article
DeepChance-OPT: A Robust Decision-Making Framework for Dynamic Grasping in Precision Assembly
by Tong Wei and Haibo Jin
Information 2026, 17(2), 187; https://doi.org/10.3390/info17020187 - 12 Feb 2026
Viewed by 369
Abstract
Achieving safe and efficient sequential decision-making in dynamic and uncertain environments is a core challenge in intelligent manufacturing and robotic systems. During operation, systems are often subject to coupled multi-source uncertainties—such as stochastic disturbances, model mismatch, and environmental shifts—rendering traditional approaches based on [...] Read more.
Achieving safe and efficient sequential decision-making in dynamic and uncertain environments is a core challenge in intelligent manufacturing and robotic systems. During operation, systems are often subject to coupled multi-source uncertainties—such as stochastic disturbances, model mismatch, and environmental shifts—rendering traditional approaches based on deterministic models or post hoc safety verification incapable of simultaneously ensuring performance and safety. In particular, the non-differentiability of constraint satisfaction probabilities in chance-constrained decision-making severely impedes its integration with data-driven learning paradigms. To address these challenges, this paper proposes DeepChance-OPT (Deep Chance Optimization), an end-to-end differentiable disturbance-rejection decision framework tailored for dynamic grasping tasks in precision assembly. The framework first encodes historical observations and control sequences into a low-dimensional latent representation to extract key dynamic features relevant to decision-making. Subsequently, it models the temporal propagation of uncertainty in this latent space to predict the probability distribution of future states. Furthermore, via a differentiable chance-constrained mechanism, the risk of constraint violation is transformed into a continuous and differentiable penalty term, which is jointly optimized with the task performance objective to achieve synergistic improvement in both safety and efficiency. The entire framework is trained and executed under a unified end-to-end architecture, enabling closed-loop online sequential decision-making. Experiments on a precision silicon carbide wafer grasping task demonstrate that DeepChance-OPT achieves real-time performance (average decision latency < 4 ms) while reducing the constraint violation rate to 2.3%, significantly outperforming both traditional optimization and purely data-driven baselines. Under composite uncertainty scenarios—including parameter perturbations, measurement noise, and external disturbances—the success rate remains stably above 87.5%, fully validating the effectiveness of the proposed framework for robust, safe, and efficient decision-making in complex dynamic environments. This work provides a new paradigm for intelligent disturbance-rejection decision-making in high-precision manufacturing, offering both theoretical rigor and engineering practicality. Full article
(This article belongs to the Special Issue Data-Driven Decision-Making in Intelligent Systems)
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16 pages, 307 KB  
Article
Being (Not) Successful in Internationalisation After Receiving Export Support: Which Predictors Are Able to Forecast It and How Accurately?
by Oliver Lukason and Tiia Vissak
Information 2025, 16(7), 544; https://doi.org/10.3390/info16070544 - 27 Jun 2025
Cited by 1 | Viewed by 905
Abstract
This paper aims to outline which predictors are able to forecast being (not) successful in internationalisation after receiving export support and how accurately they can perform this task. Using data on export grant recipients from an Estonian export support programme, 15 theoretically motivated [...] Read more.
This paper aims to outline which predictors are able to forecast being (not) successful in internationalisation after receiving export support and how accurately they can perform this task. Using data on export grant recipients from an Estonian export support programme, 15 theoretically motivated predictors grouped into four domains are used to forecast 24 different proxies of (non-)success with logistic regression and neural networks. The domains focus on firms’ general characteristics, earlier financial and export performance, and export-grant-specific characteristics. The highest areas under the curve exceed the 0.9 threshold, therefore indicating excellent predictive abilities, while more specific (non-)success proxies can be predicted less accurately than general ones. Predictors portraying firm size and export support size emerge as the best in the case of both methods, while in different neural networks, at least one predictor from each of the four domains is among the most important ones. These results lead to multiple practical implications concerning how to select firms into export grant programmes. Full article
(This article belongs to the Special Issue Data-Driven Decision-Making in Intelligent Systems)
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Other

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53 pages, 625 KB  
Systematic Review
The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review
by Tim Nolte and Sven Tomforde
Information 2025, 16(6), 496; https://doi.org/10.3390/info16060496 - 14 Jun 2025
Viewed by 2137
Abstract
Condition Monitoring (CM) is a key component of Self-Adaptive and Self-Organizing (SASO) systems. By analyzing sensor data, CM enables systems to react to dynamic conditions, supporting the core principles of Organic Computing (OC): robustness, adaptability, and autonomy. This survey presents a structured overview [...] Read more.
Condition Monitoring (CM) is a key component of Self-Adaptive and Self-Organizing (SASO) systems. By analyzing sensor data, CM enables systems to react to dynamic conditions, supporting the core principles of Organic Computing (OC): robustness, adaptability, and autonomy. This survey presents a structured overview of CM techniques, application areas, and input data. It also assesses the extent to which current approaches support self-* properties, real-time operation, and predictive functionality. Out of 284 retrieved publications, 110 were selected for detailed analysis. About 38.71% focus on manufacturing, 65.45% on system-level monitoring, and 6.36% on static structures. Most approaches (69.09%) use Machine Learning (ML), while only 18.42% apply Deep Learning (DL). Predictive techniques are used in 16.63% of the studies, with 38.89% combining prediction and anomaly detection. Although 58.18% implement some self-* features, only 42.19% present explicitly self-adaptive or self-organizing methods. A mere 6.25% incorporate feedback mechanisms. No study fully combines self-adaptation and self-organization. Only 5.45% report processing times; however, 1000 Hz can be considered a reasonable threshold for high-frequency, real-time CM. These results highlight a significant research gap and the need for integrated SASO capabilities in future CM systems—especially in real-time, autonomous contexts. Full article
(This article belongs to the Special Issue Data-Driven Decision-Making in Intelligent Systems)
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