Modeling the Dispute Settlement of Reservoir In View of the Water Quality Issues: A Novel Hybrid Group Game Theory-Fuzzy Logic Approach
Bibliographic record
Abstract
Abstract To choose the best policy of the water resources allocation, considering both the quantitative and qualitative factors based on the possible uncertainties, there has always been a significant problem in the dry lands from perspectives of the decision makers. In this paper, using Game Theory concept, a novel hybrid approach of the Game Theory based fuzzy logic is proposed to reconcile conflicts among stakeholders of dam reservoirs who have their own different strategies and utilities to choose the best policy in order to gain the highest profit regarding the situations they deal with. In the proposed method, after the fuzzification of decision makers’ strategies, a matrix called a “priority matrix” is formed in which a combination of their strategies and priorities is presented as the matrix elements. Based on the concept of Nash equilibrium, an optimized scenario is elected from among the bargaining scenarios constructed via the integrated strategies. This method has the privilege of providing a game space in which a large variety of strategies and priorities of many decision makers can be modeled in a fuzzy space of variables to reach a point of agreement. In this study, the 15-Khordad Dam of Iran is selected as a case study area which faces problems such as salinity, low quality water, and conflicts among stakeholders. The results of the study indicated that the proposed method could be led to an optimized strategy for the water resources allocation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".