Application of multi-agent decision-making methods in hydrological ecosystem services management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a methodology is presented for managing hydrological ecosystem services by taking into account the hierarchy of stakeholders involved in the decision-making process. With this in mind, a water allocation model is first used for allocating water resources to demands. Then, several ecosystem services (ESs)-based criteria are defined to evaluate hydrological ESs of water resources management policies. A set of water and environmental resources management strategies (alternatives) are defined for decision-makers, and several drought management strategies are determined to decrease the area of key crops and water demands of agricultural nodes. To model a multi-agent multi-criteria decision-making problem for managing hydrological ESs, three main steps are considered as follows:•Different ES-based criteria (i.e., economic profit, NPP, and ecological index) are defined, and their grade-based values are estimated.•Several strategies are defined for stakeholders at different levels.•A recursive evidential reasoning (ER) approach, which considers a hierarchical structure for decision-makers and a leader-follower game, is used to select the best strategy for each decision-maker.The applicability and efficiency of the methodology are illustrated by applying it to a real-world case study. The methodology is general and can be easily applied to other study areas.
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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 | 0.001 | 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 | 0.000 | 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