Graph Model Conflict Resolution Approach for Jordan River Basin Dispute
Bibliographic record
Abstract
This paper aims to establish a practical conflict resolution mechanism and applies it to solve the strategic long-term dispute for Jordan River Basin.The paper starts with a brief history of the Jordan River Basin dispute.The paper then presents a game theoretic approach based on the Graph Model technique for conflict resolution, to investigate the Jordan River Basin dispute, considering the complex socio-political aspects involved.The proposed model of defines the courses of actions available to all the involved stakeholders along with their preferences among them.Accordingly, the model applies stability and sensitivity analyses to propose an optimum resolution to the conflict and to examine the sensitivity of such resolution to the uncertainty in stakeholders' preferences.In this study, three scenarios were investigated with different coalition possibilities among different countries, as follow: (i) Syria, Lebanon, Israel, and Jordan; (ii) Lebanon, Jordan, Israel, and Palestine; and (iii) Jordan, Israel, and Palestine.The results suggest that the best resolution for all parties is through combined water and peace treaties.The results also indicate that a peace treaty between Israel and Palestine is the best resolution to the conflicts.The application of the Graph model in this paper shows its practicality and ability to provide each decision maker with a simulation environment to examine the actions and counteractions that take place during the negotiation among the different parties.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".