Mixed Coalitional Stabilities With Full Participation of Sanctioning Opponents Within the Graph Model for Conflict Resolution
Bibliographic record
Abstract
Mixed coalition analysis approaches are developed within the framework of the graph model for conflict resolution (GMCR) for analyzing the heterogeneous multicoalitional opponents having mixed coalitional sanctions, in which some sanctioning coalitions may only invoke coalitional improvements (CIs) while others may go to any reachable states to jointly sanction a focal coalition's CIs. To accomplish this, the concept of a full coalition set is defined in which each participating decision maker (DM) is represented once either as an individual player or part of a coalition. All possible scenarios in which a full coalition set could be formed is called the universal coalition set. When calculating the stability of a specific state for a particular coalition in a given conflict, the remaining DMs form the universal coalition set whose moves have the potential to block any CIs by the focal coalition within four specified solution concepts having full participation of sanctioning opponents. To handle heterogeneous opponents with mixed CIs (MCIs), the mixed coalition analysis approach is proposed, which provides a more general coalition analysis framework than existing coalition analysis approaches. Moreover, the interrelationships among coalitional stabilities and mixed coalitional stabilities with full participation are investigated followed by their corresponding matrix representations which can significantly improve their computational efficiency and make the computer implementation possible. Finally, a case study is investigated to demonstrate how to employ the proposed mixed coalition analysis approaches to address a real-world environmental conflict.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".