We show that participation decisions cannot be reduced to forecast functions in dynamic environments. When the underlying system exhibits state persistence, transition risk, and path dependence, forecastonly policies fail to satisfy Bellman optimality. Using a Markov Decision Process (MDP) formulation, we characterize forecast-only policies as mappings of the form 𝑎 𝑡 = 𝑓(𝑟̂𝑡) and show that they ignore the endogenous evolution of the state space, so that current actions influence future states and thus continuation value. We establish Theorem 3 (Dynamic Participation Optimality): the class of forecast-only policies is strictly dominated by state-path dependent policies of the form 𝑎 𝑡 = 𝜋(𝑟̂𝑡, 𝑠 𝑡 , ℎ 𝑡). This result implies that participation must be modelled as a distinct control variable, structurally separated from opportunity evaluation. The findings suggest that many existing quantitative frameworks are not merely suboptimal but structurally mis-specified, as they collapse dynamic control problems into static prediction tasks. We conclude that optimal decision-making in such environments requires a layered architecture in which participation governance is explicitly modelled as a function of state and path variables.