The Graph Model for Conflict Resolution and Credible Maximin Stability
Bibliographic record
Abstract
In strategic conflicts, the behavior of decision makers (DMs) is affected by their beliefs about how their opponents will respond, how they will counter respond, and more generally by the number of action-reaction steps they consider. In this article, we propose several notions of stability with variable horizon that vary in whether the focal DM and her opponents are allowed to make moves that are immediately disadvantageous, called unilateral disimprovements. Credible maximin stability reflects the assumption that opponents will attempt to deter a focal DM from a move by imposing the strongest possible sanction. We analyze the effects of varying the horizon on this form of stability, and on the relationships among the solution concepts that result. Requiring the focal DM to make only unilateral improvements does not influence the stability of states, but restricting opponents’ responses to unilateral improvements reduces the set of stable states. An application to a water-pricing conflict illustrates the analysis of a graph model using credible maximin stabilities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".