The integration of machine learning (ML) techniques into social sciences represents a paradigmatic shift in how researchers approach decision-making processes and behavioral analysis. This comprehensive review examines the evolving landscape of ML applications in social sciences, exploring how computational methods are reshaping traditional research methodologies and opening new avenues for understanding human behavior. We analyze current applications across psychology, sociology, economics, and political science, identifying key methodological innovations and their implications for decision-making paradigms. The paper synthesizes recent developments in behavioral prediction, social network analysis, and computational social science, while highlighting emerging research directions that promise to transform our understanding of social phenomena. Our analysis reveals significant opportunities for interdisciplinary collaboration and identifies critical gaps that warrant future investigation. The findings suggest that ML-driven approaches not only enhance predictive accuracy but also provide novel theoretical insights into the complex dynamics of human decision-making in social contexts.
Keywords: machine learning, social sciences, decision-making, computational social science, behavioral prediction, research methodology.
Citation: Celeste C. (2025). Machine Learning and New Decision-Making Paradigms in Social Sciences: New Perspectives of Research. J AI & Mach Lear., 1(2):1-9.