Researchers in computational psychiatry, addiction medicine, dynamical systems, and Active Inference.
A generalized generative-model framework for addiction and recovery dynamics.
Lead: Dr. Martin Bello
Regime-Dependent Active Inference develops a generalized generative-model framework combining continuous latent cognitive-affective states with discrete regime transitions to model the dynamics of addiction, relapse, and recovery.
The framework extends standard single-regime Active Inference by modeling the addictive cycle as transitions among inferred operating regimes (recovery/Wise Mind, emotional destabilization, cognitive capture, policy collapse, and aftermath) while preserving core variational and expected free energy principles.
Researchers in computational psychiatry, stochastic systems, control theory, and clinical addiction research are invited to collaborate on mathematical formulations, simulation models, and empirical evaluations.
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