Addiction as a Disruption of the Markov Blanket: A Hyper-Precision of Momentary Rewards Over Long-Term Adaptation

This opinion piece proposes that addiction can be conceptualized as a disruption of an agent’s Markov blanket - the statistical boundary separating internal states from the external environment - under active inference. Addiction arises when the Markov blanket becomes overly rigid, assigning excessive precision to a narrow set of sensory inputs and active states associated with the addictive stimulus. This leads to compulsive behaviors aimed at immediate rewards while sacrificing the exploration of flexible action policies that would reduce uncertainty and optimize the agent’s long-term fit with the environment. The addicted agent becomes trapped in a limited behavioral repertoire, unable to adapt to volatility, thus eroding its own resilience. Rigid, compulsive patterns of interaction can propagate outwards, undermining the resilience and sustainability of the wider ecosystem. This perspective offers an integrative framework for understanding addictive disorders and their broader impacts, suggesting interventions aimed at pulling the Markov blanket away from problematic attractors and promoting adaptive exploration.