Progress in modeling dynamic systems for sustainable development
Bibliographic record
Abstract
This Perspective evaluates recent progress in modeling nature-society systems to inform sustainable development. We argue that recent work has begun to address longstanding and often-cited challenges in bringing modeling to bear on problems of sustainable development. For each of four stages of modeling practice-defining purpose, selecting components, analyzing interactions, and assessing interventions-we highlight examples of dynamical modeling methods and advances in their application that have improved understanding and begun to inform action. Because many of these methods and associated advances have focused on particular sectors and places, their potential to inform key open questions in the field of sustainability science is often underappreciated. We discuss how application of such methods helps researchers interested in harnessing insights into specific sectors and locations to address human well-being, focus on sustainability-relevant timescales, and attend to power differentials among actors. In parallel, application of these modeling methods is helping to advance theory of nature-society systems by enhancing the uptake and utility of frameworks, clarifying key concepts through more rigorous definitions, and informing development of archetypes that can assist hypothesis development and testing. We conclude by suggesting ways to further leverage emerging modeling methods in the context of sustainability science.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".