Quantitative Analysis and Mathematical Modeling in Economics and Financial Decision-Making

Dear Colleagues,

This Special Issue aims to highlight the essential role of mathematical tools and quantitative methods in understanding and optimizing decision-making processes in key areas of the contemporary economy. We welcome contributions that demonstrate the power of data-driven approaches and mathematical modeling in addressing major economic, financial, and societal challenges.

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

• Transition to a circular and sustainable economy;
• The influence of religious and cultural values on economic and managerial decisions;
• Strategic leadership and financial governance in key economic sectors;
• Predictive analytics and risk modeling in financial systems;
• Integration of digital technologies and artificial intelligence in economic analysis and planning;
• The role of quantitative tools in evaluating economic performance and sustainability.

In a world increasingly shaped by digital transformation, exploring how quantitative analysis can support innovation, efficiency, and resilience in economic systems has become essential. We encourage interdisciplinary research that bridges mathematics, economics, data science, and social sciences to generate practical and impactful insights.

The main objectives of this Special Issue are as follows:

• Promote interdisciplinary research applying mathematical and quantitative methods in economic and financial contexts;
• Encourage the development of mathematical models to support strategic decision-making, risk assessment, and performance evaluation;
• Explore how cultural, ethical, and technological dimensions can be integrated into economic modeling and financial analysis;
• Foster innovative approaches to data science, big data analytics, and economic forecasting with societal relevance.

We invite submissions of theoretical articles, empirical studies, case analyses, and innovative methodological contributions from researchers working in fields such as those listed below:

• Applied mathematics;
• Economics and finance;
• Data science and artificial intelligence;
• Sustainability studies;
• Risk management and financial modeling.

Prof. Dr. Kamer-Ainur Aivaz
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 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. Mathematics 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 2600 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.

• quantitative analysis
• mathematical modeling
• decision-making
• economics
• finance
• sustainable economy
• circular economy
• risk modeling
• predictive models
• data science
• digital transformation
• innovation in economics
• performance evaluation
• financial governance
• religious and cultural values
• big data
• interdisciplinarity