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Reinterpreting stochastic optimal control under ecological uncertainty: Inferring decision urgency from vegetation
Komi Mensah Agboka1,2, Tobias Landmann1, Elfatih M Abdel-Rahman1,3
1International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772 00100, Nairobi, Kenya.
None:
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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