Adaptive network dynamics and behavioral contagion in multi-state drug use propagation

Addictive behavior spreads through social networks via feedback among choice, peer pressure, and shifting ties, a process that eludes standard epidemic models. We present a comprehensive multi-state network model that integrates utility-based behavioral transitions with adaptive network rewiring, capturing the co-evolutionary dynamics between drug use patterns and social structure. Our framework distinguishes four distinct individual states by combining drug use behavior with addiction status, while allowing individuals to strategically disconnect from drug-using neighbors and form new connections with non-users. Monte Carlo simulations show that rewiring reshapes contagion, pulling high-degree nodes into drug-free clusters and stranding users on sparse fringes. Systematic exploration of the four-dimensional parameter space reveals strong parameter sensitivity and nonlinear regime transitions, where modest changes in recovery rates or addiction conversion rates produce measurable shifts in population-level outcomes across finite parameter intervals. Most significantly, the rewiring probability emerges as the dominant control parameter, establishing adaptive network management as more influential than biological susceptibility factors in determining addiction prevalence. Our findings challenge traditional intervention paradigms by revealing that empowering individuals to curate their social environments may be more effective than targeting individual behavioral change alone.