Deeply ingrained habits must completely destabilize into a state of chaos before they can ever be replaced by a more efficient way of living.
Deeply ingrained habits often act like deep ruts that prevent any meaningful change. Standard models suggest that gradual, steady improvements are the best path toward progress. This specific reinforcement learning model shows that an agent must undergo decanalization, which is a total loss of stability, to reach a better state. Agents that remained stable throughout the simulation stayed trapped in sub-optimal routines forever. Temporary chaos allows the system to explore more efficient homeostatic patterns that are otherwise inaccessible. Personal or social crises may actually be the mechanical requirement for breaking bad cycles and finding a better way to survive.
Modeling Decanalization with Homeostatic Reinforcement Learning
PsyArXiv · y4x29_v1
Living organisms continuously manage their homeostatic setpoints but sometimes fall into the ruts and grooves of an entrenched or canalized behavioral repertoire. This manifests in a wide variety of ways, from overconsumption of scarce resources to repetitive unhealthy behavior, in cases of depression or anxiety. Clinicians are increasingly employing interventions, from psychoactive drugs to targeted stimulus regimes, to transiently decanalize entrenched behaviors and recanalize them into more a