Representations of status quo analysis in the graph model for strength of preference
Bibliographic record
Abstract
In this paper, status quo analysis is addressed by using both logical and matrix representations in the graph model with strength of preference. The graph model for conflict resolution (GMCR) provides a convenient and effective means to model and analyze a strategic conflict. The graph model has entertained diverse preference structures to characterize decision-makers' (DMs) preference over feasible states, including simple preference, preference uncertainty, and strength of preference. The ¿simple preference¿ structure consists of a strict preference relation and an indifference relation to represent a DM's preference for one state relative to another. Another ¿strength of preference¿ framework allows a DM to express its strong or mild preference for one state over another, as well as the indifference relation. When a graph model is established for a strategic conflict, the standard practice is to carry out a stability analysis first, and then, followed by a post-stability analysis such as coalition analysis and status quo analysis. Status quo analysis complements stability analysis and aims at assessing whether predicted equilibria are attainable from the status quo. So far, status quo analysis has been examined for the graph model with simple preference and both logical and matrix representations of status quo analysis have been developed. This article extends these results to handle status quo analysis for the graph model with strength of preference. The algebraic method is illustrated using a conflict over proposed bulk water exports from Lake Gisborne in Newfoundland.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".