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Literature Review Volume 9 Issue 1
Practical problems and challenges in mathematical modeling in social sciences research
Loso Judijanto
Received: July 10, 2026 | Published: August 4, 2026
Citation: Judijanto L. Practical problems and challenges in mathematical modeling in social sciences research. Open Access J Sci. 2026;13(1):375-386. DOI: 10.15406/oajs.2026.13.00321
Abstract
The growing use of mathematical modeling in the social sciences has significantly expanded analytical capabilities to understand complex social phenomena. However, despite its increasing adoption, researchers frequently encounter practical and methodological difficulties when applying mathematical models to dynamic and multidimensional social systems. This study aims to systematically examine the principal practical problems and methodological challenges associated with applying mathematical modeling in contemporary social science research through a structured synthesis of recent scholarly literature. The research employs a Systematic Literature Review (SLR) approach to ensure transparent and replicable analysis of existing studies. Data were collected through a systematic search of the Scopus database using keyword combinations related to mathematical modeling, the social sciences, and methodological challenges. The screening and eligibility process yielded a final dataset of 32 peer-reviewed articles published between 2019 and 2026 that met all predefined inclusion criteria. The selected studies were analyzed using thematic synthesis to identify recurring methodological patterns and constraints reported in the literature. The analysis reveals five dominant categories of challenges: structural simplification of social behavior within models, uncertainty in parameter estimation, limitations in the availability of empirical data and measurement reliability, difficulties in empirical model validation, and interdisciplinary barriers to integrating mathematical techniques with social science theory. These findings indicate that while mathematical modeling provides valuable analytical insights, its effective application requires careful attention to methodological transparency, data quality, and interdisciplinary collaboration. Future research should focus on improving model validation frameworks, expanding the integration of empirical data, and strengthening interdisciplinary methodological training.
Keywords: mathematical modeling, social sciences, systematic literature review, methodological challenges, computational social science
©2026 Judijanto. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.