Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.5
2.9
Journals
Information
Special Issues
Information Management and Decision-Making
share announcement

Information Management and Decision-Making

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2023

Special Issue Editor

Prof. Dr. Robert F. Bordley

E-Mail Website
Guest Editor
College of Engineering, University of Michigan, 1075 Beal Avenue, Ann Arbor, MI 48109, USA
Interests: decision analysis; probabilistic risk analysis; bayesian inference; bayesian probability; decision theory; foundations of quantum mechanics

Special Issue Information

Dear Colleagues,

Individuals increasingly ask AI for advice on making difficult decisions. Decision analysis and decision sciences develop methods to help an individual make decisions under conditions of uncertainty in a manner consistent with that person’s fundamental objectives. The techniques of decision analysis were originally developed and refined by consultants who worked closely with their clients to elicit their beliefs and fundamental values.

This Issue focuses on how to incorporate the knowledge from the decision sciences into artificially intelligent software. Some decision analytic methods, e.g., collecting data on risks similar to the risks faced by the decision maker, can already be performed by AI. It may be more challenging to incorporate other methods, e.g., reading body language and facial. As a result, innovations in both decision analysis and AI may be needed. This Issue welcomes papers that contributing on these and related issues, providing a forum to bridge AI and the decision sciences.

Topics of interest include, but are not limited to, the following areas:

  • AI-driven methods for preference elicitation and utility modeling;
  • Decision-theoretic reasoning under uncertainty in AI architectures;
  • Probabilistic reasoning and scenario planning for high-stakes decision support;
  • Human–AI collaborative decision processes: interactive inference and iterative refinement;
  • AI systems implementing decision-analytic techniques in domains such as healthcare, finance, engineering, or public policy;
  • Multimodal cue integration in AI-assisted decisions;
  • Hybrid architectures combining machine learning and normative decision models;
  • Evaluations of AI-involved decision outcomes in uncertain or safety-critical settings;
  • Sequential decision-making and dynamic decision processes in AI systems;
  • Calibration and validation of AI-based decision support under model uncertainty.

Prof. Dr. Robert F. Bordley
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

  • artificial intelligence
  • decision analysis
  • decision science
  • artificially intelligent software
  • risk assessment

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 2649 KB  
Article
A Bayesian-Optimized XGBoost Approach for Money Laundering Risk Prediction in Financial Transactions
by Zihao Zuo, Yang Jiang, Rui Liang, Jiabin Xu, Hong Jiang, Shizhuo Zhang, Yunkai Chen and Yanhong Peng
Information 2026, 17(4), 324; https://doi.org/10.3390/info17040324 - 26 Mar 2026
Viewed by 447
Abstract
The rapid expansion of global commerce has escalated the complexity of money laundering schemes, making the detection of illicit transfers an urgent but highly challenging research problem. In operational anti-money laundering (AML) systems, the extreme rarity of illicit transactions often overwhelms compliance teams [...] Read more.
The rapid expansion of global commerce has escalated the complexity of money laundering schemes, making the detection of illicit transfers an urgent but highly challenging research problem. In operational anti-money laundering (AML) systems, the extreme rarity of illicit transactions often overwhelms compliance teams with false positives, leading to severe “alert fatigue.” To address this critical bottleneck, this paper introduces an enhanced, probability-driven risk-prioritization framework utilizing an XGBoost classifier integrated with Bayesian Optimization (BO-XGBoost). By optimizing directly for the Area Under the Precision–Recall Curve (PR-AUC), the model is specifically tailored to rank high-risk anomalies under severe class imbalance. We validate the proposed approach on a rigorously resampled transaction dataset simulating a realistic 5% laundering rate. The BO-XGBoost model demonstrates exceptional prioritization capability, achieving an ROC-AUC of 0.9686 and a PR-AUC of 0.7253. Most notably, it attains a near-perfect Precision@1%, meaning the top 1% of flagged transactions are 100% true illicit activities, entirely eliminating false positives at the highest priority tier. Comparative and SHAP-based interpretability analyses confirm that BO-XGBoost easily outperforms sequence-heavy deep learning baselines. Crucially, it matches computationally expensive stacking ensembles in peak predictive precision while significantly surpassing them in operational efficiency, indicating its immense promise for resource-optimized, real-world compliance screening. Full article
(This article belongs to the Special Issue Information Management and Decision-Making)
►▼ Show Figures

Graphical abstract

16 pages, 2202 KB  
Article
A Hybrid Ensemble Machine Learning Framework with Membership-Function Feature Engineering for Non-Invasive Prediction of HER2 Status in Breast Cancer
by Hassan Salarabadi, Dariush Salimi, Seyed Sahand Mohammadi Ziabari and Mozaffar Aznab
Information 2026, 17(3), 296; https://doi.org/10.3390/info17030296 - 18 Mar 2026
Viewed by 281
Abstract
Accurate determination of human epidermal growth factor receptor 2 (HER2) status is a critical component of breast cancer prognosis and treatment planning. Conventional diagnostic techniques, such as immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), are clinically established but remain invasive, time-consuming, costly, [...] Read more.
Accurate determination of human epidermal growth factor receptor 2 (HER2) status is a critical component of breast cancer prognosis and treatment planning. Conventional diagnostic techniques, such as immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), are clinically established but remain invasive, time-consuming, costly, and sensitive to pre-analytical and interpretative variability. Motivated by the need for scalable and data-driven decision-support tools, this study proposes a hybrid ensemble machine learning framework for non-invasive HER2 status prediction using routinely available clinical and immunohistochemical features. A retrospective dataset comprising 624 breast cancer patients from Mahdieh Clinic (Kermanshah, Iran) was analyzed using a structured preprocessing pipeline including normalization and class balancing. The proposed framework integrates multiple tree-based classifiers, Random Forest, XGBoost, and LightGBM, through ensemble strategies and enhances predictive robustness using membership-function feature engineering to capture gradual transitions in clinically relevant biomarkers. Decision threshold optimization was further applied to improve classification balance in borderline cases. The proposed ensemble framework achieved an accuracy of 0.816, an F1-score of 0.814, and an area under the receiver operating characteristic curve (AUC) of 0.862 on a held-out test set, demonstrating performance comparable to the best-performing individual classifier. These results indicate that ensemble learning combined with smooth membership-based feature representations can provide a reliable decision-support framework for HER2 status prediction, although further external validation is required before clinical use. Full article
(This article belongs to the Special Issue Information Management and Decision-Making)
►▼ Show Figures

Figure 1

33 pages, 7152 KB  
Article
DRADG: A Dynamic Risk-Adaptive Data Governance Framework for Modern Digital Ecosystems
by Jihane Gharib and Youssef Gahi
Information 2026, 17(1), 102; https://doi.org/10.3390/info17010102 - 19 Jan 2026
Viewed by 947
Abstract
In today’s volatile digital environments, conventional data governance practices fail to adequately address the dynamic, context-sensitive, and risk-hazardous nature of data use. This paper introduces DRADG (Dynamic Risk-Adaptive Data Governance), a new paradigm that unites risk-aware decision-making with adaptive data governance mechanisms to [...] Read more.
In today’s volatile digital environments, conventional data governance practices fail to adequately address the dynamic, context-sensitive, and risk-hazardous nature of data use. This paper introduces DRADG (Dynamic Risk-Adaptive Data Governance), a new paradigm that unites risk-aware decision-making with adaptive data governance mechanisms to enhance resilience, compliance, and trust in complex data environments. Drawing on the convergence of existing data governance models, best practice risk management (DAMA-DMBOK, NIST, and ISO 31000), and real-world enterprise experience, this framework provides a modular, expandable approach to dynamically aligning governance strategy with evolving contextual factors and threats in data management. The contribution is in the form of a multi-layered paradigm combining static policy with dynamic risk indicator through application of data sensitivity categorization, contextual risk scoring, and use of feedback loops to continuously adapt. The technical contribution is in the governance-risk matrix formulated, mapping data lifecycle stages (acquisition, storage, use, sharing, and archival) to corresponding risk mitigation mechanisms. This is embedded through a semi-automated rules-based engine capable of modifying governance controls based on predetermined thresholds and evolving data contexts. Validation was obtained through simulation-based training in cross-border data sharing, regulatory adherence, and cloud-based data management. Findings indicate that DRADG enhances governance responsiveness, reduces exposure to compliance risks, and provides a basis for sustainable data accountability. The research concludes by providing guidelines for implementation and avenues for future research in AI-driven governance automation and policy learning. DRADG sets a precedent for imbuing intelligence and responsiveness at the heart of data governance operations of modern-day digital enterprises. Full article
(This article belongs to the Special Issue Information Management and Decision-Making)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop