A multi-agent decision-making framework for evaluating water and environmental resources management scenarios under climate change
Bibliographic record
Abstract
This paper introduces a hierarchical multi-agent decision-making framework for Water and Environmental Resources Management Scenarios (WERMSs) under uncertain conditions of climate change and complex agent characteristics. The proposed framework utilizes three Game Theory concepts: the Stackelberg, Bayesian (Incomplete), and Imperfect games, in order to incorporate the hierarchical structure of the agents and the temporal distribution and accuracy of information between them. The methodology is applied to the Zarrinehroud River Basin (ZRB), the largest hypersaline lake in the Middle East. The area of the lake has decreased dramatically (about 50 %) during past decades causing various environmental, social, and economic problems. WERMSs were evaluated using qualitative and quantitative hydrological, social, economic, and ecological criteria under different climate change scenarios. The proposed methodology provides equilibriums in the decision-making process while considering different climate change scenarios. Applying the selected WERM results in an accumulated value of 2995 million m 3 of water flow to the lake until 2049. Moreover, the lake's elevation reaches a new level of 1272.6 m above sea level at the end of the following 30 years, compared to the elevation of 1271.3 at the beginning of the evaluation period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".