Sharing a Multi-National Resource through Bankruptcy Procedures
Bibliographic record
Abstract
Bankruptcy procedures are known as fair division methods applicable to monetary problems in which the total amount of the asset is not sufficient to cover the sum of the creditor's claims. These methods can be also used to solve natural resource allocation problems with the same characteristics in which the bargainers are willing to follow a cooperative approach rather than a competitive attitude. To show the applicability of these methods in water resources allocation problems, this study builds a bankruptcy model for the Caspian Sea negotiations in which five coastal states of Azerbaijan, Iran, Kazakhstan, Russia and Turkmenistan have been negotiating since 1993 without coming up with any agreement neither on the ownerships of waters, nor the oil and natural gas beneath them. In this problem, the total value of oil and natural gas which are currently claimed by the five littoral states is approximately 32 percent higher than the total value of proven and possible oil and gas located in the seabed of the Caspian Sea. The problem is how to determine a fair resource allocation which is associated with the legal status of the Caspian Sea. The developed bankruptcy model is solved with four different allocation rules including Proportional rule, Constrained Equal Award (CEA) rule, Contested Garment Principle, and Adjusted Proportional (AP) rule. Based on the shares of the bargainers under these rules, each party can rank the possible sharing methods. Finally, the study comes up with some recommendations on how to allocate this multi-national water resource to the involved parties based on claims and preferences.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".