Optimal water management and conflict resolution: The Middle East Water Project
Bibliographic record
Abstract
In many situations, actual water markets will not allocate water resources optimally, largely because of the perceived social value of water. It is possible, however, to build optimizing models which, taking account of demand as well as supply considerations, can substitute for actual markets. Such models can assist the formation of water policies, taking into account user‐supplied values and constraints. They provide powerful tools for the system‐wide cost‐benefit analysis of infrastructure; this is illustrated by an analysis of the need for desalination in Israel and the cost and benefits of adding a conveyance line. Further, the use of such models can facilitate cooperation in water, yielding gains that can be considerably greater than the value of the disputed water itself. This can turn what appear to be zero‐sum games into win‐win situations. The Middle East Water Project has built such a model for the Israeli‐Jordanian‐Palestinian region. We find that the value of the water in dispute in the region is very small and the possible gains from cooperation are relatively large. Analysis of the scarcity value of water is a crucial feature.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".