Allocating water resources in transboundary river basins: A sequential rubinstein bargaining approach with risk discounting
Bibliographic record
Abstract
Study region The Mekong River Basin Study focus This study reduces the multi-agent bargaining game to a one-to-one model by assuming downstream countries act as coalitions in water allocation scenarios. Each country’s risk level and perception inform its discount factor, which is then aggregated and converted into coalition discount factors through weighted averaging. Then, a Rubinstein bargaining water allocation model with multi-agent participation and multi-stage negotiation is constructed and applied to allocate water in the Mekong River Basin. New hydrological insights for the region The proposed Multi-stage Rubinstein Bargaining Model produced allocations that were more stable than those generated by traditional bankruptcy rules such as Proportion, Adjusted Proportion, Constrained Equal Loss, Constrained Equal Award, and Shapley. Therefore, this allocation framework can serve as both a theoretical foundation and a practical tool for water allocation in transboundary river basins.
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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 | 0.006 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".