No matter how angry, sad, or happy we are, eventually, we will feel different. Studying this ebb and flow of affective experience in daily life provides important insights into psychological functioning and well-being. We have developed a parsimonious formalized model of intraindividual variability in affect (MIVA), resting on the assumption that such affective changes reflect transactions between an individual and their proximal environment. We provide an outline of its theoretical background, scope, and mathematical formulation. We situate MIVA within the research field of affect dynamics and illustrate the models’ behavior under realistic conditions using a simulation study. We use simulation-based inference to train a custom neural network on MIVA simulations, which we employ to rapidly estimate the model’s parameters on a multitude of synthetic experiments with different configurations. Our simulation study demonstrates that the synthesis between a computational model and probabilistic neural networks results in an efficient and flexible tool for model-based inference of affect dynamics. Our simulation study also offers insights into the data requirements for a precise recovery of the model’s parameters and recommendations for future data collection. The potential of MIVA for providing insights into affect dynamics is discussed.
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