RESILIENT FOOD-BIODIVERSITY OUTCOMES VIA STOCHASTIC CONTROL OF MULTIPLEX SOCIO-ECOLOGICAL NETWORKS UNDER WATER STRESS
Why this work is in the frame
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Bibliographic record
Abstract
Food-security pressures, biodiversity loss, and chronic water scarcity interact to erode the connectivity that keeps agricultural socio-ecological systems (SES) functional. We ask: how much effort — of which type and when — is required to preserve multiplex connectivity under volatile water supplies at minimum cost? We model the agricultural SES as a multiplex network and embed its dynamics in a stochastic optimal-control problem solved in Hamiltonian form. Shadow prices of connectivity are derived via the Feynman–Kac representation, and open-loop solutions are refined with a machine learning controller. Methodologically, this integrates stochastic co-states with policy refinement for multilayer SES control. Conceptually, resilience is operationalized through network-level criteria. Numerical experiments under escalating drought show: (i) optimally configured controllers maintain strong resilience under moderate stress; (ii) beyond a critical drought threshold, only weak resilience is attainable; (iii) control effort exhibits layer asymmetry, with agri-food requiring sustained torque and biodiversity benefiting from punctuated interventions; and (iv) a governance wedge persists between technically cost-effective effort and stakeholders’ willingness to implement it. These results clarify when, and how, incentive-compatible policies are needed to keep agri-food-biodiversity connectivity viable under water volatility.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it