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Modeling in the Era of Generative AI
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Modeling in the Era of Generative AI

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1420

Special Issue Editors

Dr. Tania Yankova

E-Mail Website
Guest Editor
Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria
Interests: mathematical modeling; fuzzy logic; optimization
Prof. Dr. Galina Ilieva

E-Mail Website
Guest Editor
Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria
Interests: information systems and technologies; business intelligence; big data; intelligent software agents; machine learning; data mining; multi-criteria decision making; fuzzy sets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of rapidly evolving generative artificial intelligence (GAI), modeling practices across scientific, engineering, and business domains are undergoing a fundamental transformation. Traditional modeling techniques, ranging from statistical forecasting to simulation and optimization, are increasingly being integrated with, or influenced by, generative AI technologies.

This Special Issue aims to explore the intersection between generative AI and modeling, highlighting new methodologies, frameworks, tools, and applications that reflect this paradigm shift. Whether through automating model development, enhancing predictive accuracy, generating synthetic data, or supporting complex decision-making processes, GAI is becoming a pivotal force in how we conceptualize and operationalize models in various fields.

We seek contributions from researchers and practitioners that focus on advancing modeling strategies in the GAI era. Submissions may cover, but are not limited to, the following topics:

  • GAI-augmented forecasting and simulation methods;
  • The integration of LLMs in data-driven and knowledge-based modeling;
  • Model explainability and interpretability using GAI tools;
  • The use of GAI for data preparation, feature engineering, and augmentation;
  • Domain-specific modeling in finance, education, healthcare, industry, and governance;
  • Ethical and practical challenges in AI-assisted model design and validation;
  • Hybrid modeling frameworks combining classical and generative approaches.

We welcome original research papers and review articles. By fostering interdisciplinary dialogue, this Special Issue seeks to improve our understanding of how generative AI is reshaping modeling paradigms and identify the opportunities and challenges that lie ahead.

Dr. Tania Yankova
Prof. Dr. Galina Ilieva
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. 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

  • generative artificial intelligence
  • modeling
  • statistical forecasting
  • simulation
  • optimization

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Published Papers (2 papers)

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Research

20 pages, 2200 KB  
Article
When Generative AI Goes to the Museum: Visual Stereotyping of Curators and Museum Spaces
by Dirk H. R. Spennemann and Wayne Robinson
Information 2025, 16(11), 936; https://doi.org/10.3390/info16110936 - 28 Oct 2025
Viewed by 378
Abstract
Based on 350 visualizations, this paper examines the depiction of museum curators by the popular generative artificial intelligence (AI) model, ChatGPT4o. While the AI-generated representations do not reiterate popular stereotypes of curators as nerdy, conservative in dress, and stuck in time, rummaging through [...] Read more.
Based on 350 visualizations, this paper examines the depiction of museum curators by the popular generative artificial intelligence (AI) model, ChatGPT4o. While the AI-generated representations do not reiterate popular stereotypes of curators as nerdy, conservative in dress, and stuck in time, rummaging through collections, they contrast sharply with real-world demographics. AI-generated imagery severely under-represents women (3.5% vs. 49–72% in reality) and disregards ethnic communities outside of Caucasian communities (0% vs. 18–36%). It not only over-represents young curators (79% vs. approx. 27%) but also renders curators to resemble yuppie professionals or people featured in fashion advertising. Stereotypical attributes are prevalent, with curators widely depicted as having beards and holding clipboards or digital tablets. The findings highlight biases in the generative AI image creation data sets, which are poised to shape an inaccurate portrayal of museum professionals if the images were to be taken uncritically at ‘face value’. Full article
(This article belongs to the Special Issue Modeling in the Era of Generative AI)
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21 pages, 2811 KB  
Article
Application of Generative AI in Financial Risk Prediction: Enhancing Model Accuracy and Interpretability
by Kai-Chao Yao, Hsiu-Chu Hung, Ching-Hsin Wang, Wei-Lun Huang, Hui-Ting Liang, Tzu-Hsin Chu, Bo-Siang Chen and Wei-Sho Ho
Information 2025, 16(10), 857; https://doi.org/10.3390/info16100857 - 3 Oct 2025
Viewed by 734
Abstract
This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative [...] Read more.
This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting. Full article
(This article belongs to the Special Issue Modeling in the Era of Generative AI)
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