Mathematical Modelling of Emotion Regulation: A Dynamic Systems Approach to Understanding Psychological Processes

Being able to manage our emotions is a core part of psychology. It affects how we feel, how we think, how we connect with other people, and the choices we make every day. Although it has been widely studied in psychology, research examining emotional regulation through quantitative and mathematical models remains scattered across various disciplines. This research seeks to bridge that gap by providing a systematic, PRISMA-informed meta-analytic review of interdisciplinary research that applies mathematical and quantitative modelling to emotional regulation. We conducted a broad search of peer-reviewed publications in major academic databases and then applied structured procedures for screening, assessing eligibility, and evaluating methodological quality based on predefined criteria. The studies included in this review use a variety of approaches, such as psychometric modelling, systems theory, probabilistic methods, and time-series analyses. The results strongly support viewing emotional regulation as a dynamic system, shaped by feedback processes, contextual influences, and changes over time. Psychometric models help to clearly define and accurately measure emotional regulation constructs, while dynamic and stochastic models illuminate how individuals maintain emotional balance, experience dysregulation, and recover across different time scales. This study also highlights how these models can be put to use in real-world settings, including educational, organisational, and mental health contexts, where they can guide assessment, support the design of interventions, and improve the prediction of outcomes.  In addition, we critically consider the ethical and cultural issues involved in turning emotional experiences into quantitative data, underscoring the importance of using mathematical tools in a responsible, sensitive, and context-aware manner. By bringing together systematic evidence synthesis and quantitative modelling, this research contributes to an interdisciplinary, predictive, and human-centred framework for understanding emotional regulation.