Research at the intersection of Mathematics and happiness offers a unique opportunity for multidimensional studies.

• Mathematical Modeling of Happiness: Develop mathematical models to represent and quantify subjective well-being and happiness. Explore the use of mathematical equations, algorithms, and statistical methods to understand the factors that contribute to happiness and predict individual and societal levels of happiness.
• Network Analysis of Social Interactions and Happiness: Apply network theory and mathematical modeling techniques to analyse the relationship between social networks, interpersonal interactions, and happiness. Investigate how network structure, social connections, and communication patterns influence individuals' subjective well-being.
• Optimisation of Happiness: Use optimisation theory and mathematical programming techniques to optimise individuals' happiness and well-being. Explore strategies for allocating resources, time, and effort to maximise overall happiness while considering constraints and trade-offs.
• Game Theory and Cooperation for Happiness: Utilise game theory to study cooperation, reciprocity, and altruism as factors contributing to happiness. Investigate how mathematical models of strategic decision-making can promote collective well-being and social harmony.
• Statistical Analysis of Happiness Data: Conduct statistical analyses of large-scale datasets on happiness and well-being to identify patterns, trends, and correlations. Apply advanced statistical methods such as regression analysis, factor analysis, and cluster analysis to uncover factors influencing happiness across different populations and cultures.
• Fractal Geometry and Psychological Well-being: Explore the application of fractal geometry to understand patterns of psychological well-being and resilience. Investigate how fractal dimensions and self-similarity relate to individuals' subjective experiences of happiness and satisfaction.
• Topology of Happiness Spaces: Study the topology and geometry of happiness spaces to understand the structure and dynamics of subjective well-being. Use topological methods to analyse the connectivity, boundaries, and attractors within these spaces and explore implications for happiness interventions and policy-making.
• Dynamical Systems and Emotional Regulation: Apply dynamical systems theory to model the dynamics of emotional regulation and mood fluctuations. Investigate how mathematical models of emotional dynamics can inform interventions for enhancing emotional well-being and resilience.
• Information Theory and Subjective Experience: Explore the application of information theory to quantify the complexity and entropy of subjective experience. Investigate how information-theoretic measures can capture the richness and diversity of emotional states, cognitive processes, and subjective perceptions of happiness.
• Machine Learning for Happiness Prediction: Develop machine learning algorithms to predict individuals' levels of happiness and well-being based on demographic, psychosocial, and behavioral factors. Explore the use of predictive modeling techniques to personalise interventions and support individualised pathways to happiness.

These research topics offer exciting opportunities for interdisciplinary collaboration between mathematicians, psychologists, economists, sociologists, and other researchers interested in understanding and promoting happiness and well-being. By integrating mathematical approaches with insights from psychology, sociology, and other fields, we can gain deeper insights into the complex dynamics of happiness and contribute to the development of evidence-based interventions for enhancing human flourishing.