A hierarchical graph model for conflict resolution with sequential moves
Bibliographic record
Abstract
A novel hierarchical graph model, called hierarchical graph model with sequential moves (SHGM), is developed to investigate interrelated conflicts in which common decision-makers (CDMs), participating in all interrelated conflicts, unilaterally move in one subconflict at a time. The preferences of decision-makers are described by a scoring system. Equilibria under different solution concepts are calculated as the indication of possible outcomes or strategic resolutions for decision-makers. This novel methodology is applied to subsidy disputes between the national government in China and two provincial governments, typical hierarchical conflicts containing sequential moves of the national government who wishes to strategically allocate the limited subsidies to encourage the compliance of carbon mitigation policies by the provinces. In-depth analysis is carried out to determine the equilibria that can be evolved from the starting state, called the status quo. Among these equilibria, the most preferred one for the national government, called the optimal equilibrium, suggests meaningful resolutions for each decision-maker. In particular, the national government in China is advised to subsidize the more industrialized province so that both provinces would comply with its carbon mitigation policies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".