RESILIENT FOOD-BIODIVERSITY OUTCOMES VIA STOCHASTIC CONTROL OF MULTIPLEX SOCIO-ECOLOGICAL NETWORKS UNDER WATER STRESS
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".