Résumé
The shape of the corresponding attractor landscape is assumed to evolve over time through interactions among three necessary and sufficient social-cognitive variables, namely competence expectancies, expected benefit for the self, and threat for the self. These variables interact within and across personal, contextual, and situational levels. The present study aimed to (a) develop an Agent-Based Models (ABM; Smaldino, 2023) capable of simulating these interactions and therefore the dynamics of approach and avoidance motivation patterns, and (b) compare the outputs of this ABM with longitudinal data from three athletes and two PhD students pursuing an important mid-term (1–2 years) goal. Whittle's Maximum Likelihood Estimator (Roume, 2023) was used to detect 1/f power-law distributions—a temporal variability typical of complex dynamical phenomena—in the time series of both simulation and ecological data. Findings revealed 1/f distributions in both types of time series, thus supporting the relevance of the CDS paradigm in understanding approach and avoidance motivation. They also pave the way for future research testing intervention hypotheses on motivation using computer simulation.