Reinterpreting stochastic optimal control under ecological uncertainty: Inferring decision urgency from vegetation biomass dynamics

Publication Type

Journal Article

Journal Name

Mathematical Biosciences and Engineering

Publication Date

1-1-2026

Abstract

Stochastic optimal control provides a rigorous framework for systems subject to uncertainty, yet its operational use in ecological crisis contexts remains limited by interpretability. We reinterpreted a stochastic control formulation in which selected parameters emerged as indicators of decision urgency rather than normative preferences. Vegetation biomass was modeled as a stochastic stock subject to nonlinear loss driven by feeding pressure (e.g., desert locust activity) and multiplicative noise, with uncertainty represented by a time-varying volatility term that integrated extreme rainfall anomalies and conflict-related disruption. Rather than prescribing an optimal policy, we inverted the closed-form solution of the control problem to infer the minimum urgency required to maintain the stock above a policy-defined threshold. Applied spatio-temporally, the framework revealed coherent monthly and regional patterns of inferred urgency, distinguishing stable regimes from disruption-dominated conditions and identifying periods in which short-term depletion overwhelms recovery.

Keywords

Brownian motion, Hamilton–Jacobi–Bellman, operational decision support, optimal control theory, Schistocerca gregaria

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