Markov models are particularly valuable for forcing clarity about assumptions because they assume that if transition probabilities are fixed and every state can reach every other state, the system will converge to a unique equilibrium—which means if someone claims their system is path-dependent or complex, they must be either creating new states or changing transition probabilities, forcing them to be explicit about their mechanisms.