The dynamics of uncertainty: a systematic review of non-linear dynamical systems in decision-making

Decision-making under uncertainty at the individual human level is crucial in fields like healthcare, finance, and everyday life. Traditional linear models often fail to capture the complexity of human decisions, as they neglect the dynamic, emotional, and contextual interplay involved. Non-linear Dynamical Systems methods provide a more nuanced perspective, highlighting how subtle emotional shifts or minor environmental changes significantly influence individual decision trajectories. This systematic review synthesizes insights from fifteen studies identified through a multi-database search with predefined inclusion criteria, employing NDS methods, such as attractor modeling and bifurcation analysis. Findings reveal that nonlinear approaches capture dynamic phenomena like abrupt decision shifts, multistability, and inherent feedback loops in human decisions under uncertainty. However, this review also identifies several limitations, including high computational complexity, interpretative challenges, and substantial data requirements, limiting practical applicability and interdisciplinary adoption. Future research should focus on standardizing methodologies, enhancing interpretability, and validating these models through real-world longitudinal studies. Addressing these challenges will improve the applicability of NDS techniques, making them more accessible for researchers and practitioners seeking more profound insights into human decision-making under uncertainty.