Structural Persistence Without Reward: Emergent Resistance to Change in Constraint-Based Systems

Persistent, repetitive behavior is commonly attributed to reward, motivation, or learning. This work presents a constraint-based computational investigation demonstrating that persistent, compulsion-like behavioral loops can emerge and resist disruption in systems entirely devoid of reward, desire, learning, goals, agency, and psychological structure. Using a global state-space simulation governed solely by neutral transition constraints, we show that stable loops spontaneously form and exhibit increasing resistance to forced escape, even when alternative trajectories are accessible. Because all motivational and reinforcement mechanisms are explicitly excluded, the observed persistence must arise from intrinsic structural properties of the state-space and its transition topology. These findings establish a no-go result: persistent behavior cannot be eliminated through perturbation alone without reintroducing preferential mechanisms. The work establishes a lower bound on the requirements for behavioral persistence and highlights structural constraints as a foundational contributor to resistance to change.