The Bayesian reservoir model of psychological regulation

Social and behavioral scientists are increasingly interested the dynamics of the processes they study. Despite the wide array of processes studied, a fairly narrow set of models are applied to characterize dynamics within these processes. For social and behavioral research to take the next step in modeling dynamics, a wider variety of models need to be considered. The reservoir model is one model of psychological regulation that helps expand the models available (Deboeck & Bergeman, 2013). The present article implements the Bayesian reservoir model for both single time series and multilevel data. Simulation 1 compares the performance of the original version of the reservoir model fit using structural equation modeling (Deboeck & Bergeman, 2013) to the proposed Bayesian estimation approach. Simulation 2 expands this to a multilevel data scenario and compares this to the single-level version. The Bayesian estimation approach performs substantially better than the original estimation approach and produces low-bias estimates even with time series as short as 25 observations. Combining Bayesian estimation with a multilevel modeling approach allows for relatively unbiased estimation with sample sizes as small as 15 individuals and/or with time series as short as 15 observations. Finally, a substantive example is presented that applies the Bayesian reservoir model to perceived stress, examining how the model parameters relate to psychological variables commonly expected to relate to resilience. The current expansion of the reservoir model demonstrates the benefits of leveraging the combined strengths of Bayesian estimation and multilevel modeling, with new dynamic models that have been tailored to match the process of psychological regulation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).