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
Recommended Citation
Agboka, K., Landmann, T., & Abdel-Rahman, E. (2026). Reinterpreting stochastic optimal control under ecological uncertainty: Inferring decision urgency from vegetation biomass dynamics. Mathematical Biosciences and Engineering, 23 (6), 1844-1868. https://doi.org/10.3934/mbe.2026067
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.