Behavioral Analysis in the Graph Model for Conflict Resolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using the observed or desired stability results of each decision maker (DM) and the equilibrium of a conflict, an algorithm is proposed that determines whether the Nash, general metarationality, symmetric metarationality or sequential stability definition best explains the observations or how to achieve the desired equilibrium. The matrix representation of the graph model for conflict resolution, normally used to determine stabilities that reflect different models of human interaction in conflict, is utilized here to keep track of available one-step moves for each DM, as well as the DMs' relative preferences among states. A key theorem involving matrix representation of graph models determines which stability types are consistent with the observations or achievement of the desired equilibrium, taking into account specified preferences of the DMs, for the case of two or more DMs. To illustrate the use of the algorithm in practice, behavioral analysis is applied to a softwood lumber dispute between Canada and the USA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it