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

Komi Mensah Agboka; Tobias Landmann; Elfatih M. Abdel-Rahman; Komi Mensah Agboka; Tobias Landmann; Elfatih M. Abdel-Rahman

doi:10.3934/mbe.2026067

Mathematical Biosciences and Engineering

2026, Volume 23, Issue 6: 1844-1868. doi: 10.3934/mbe.2026067

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Research article

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

1.
International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772 00100, Nairobi, Kenya
2.
Laboratoire de Recherche en Science et Technologie (LARSI), Département de Génie Informatique (GI), École Polytechnique de Lomé (EPL), Université de Lomé, Lomé, Togo
3.
School of Agriculture and Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa

Received: 17 February 2026 Revised: 06 April 2026 Accepted: 20 April 2026 Published: 27 May 2026

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.
- optimal control theory,
- Brownian motion,
- Hamilton–Jacobi–Bellman,
- operational decision support,
- Schistocerca gregaria
Citation: Komi Mensah Agboka, Tobias Landmann, Elfatih M. Abdel-Rahman. Reinterpreting stochastic optimal control under ecological uncertainty: Inferring decision urgency from vegetation biomass dynamics[J]. Mathematical Biosciences and Engineering, 2026, 23(6): 1844-1868. doi: 10.3934/mbe.2026067

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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.

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