The Graph Model for Conflict Resolution and Decision Support
Bibliographic record
Abstract
A survey of the design, development and implementation of a flexible decision technology called the graph model for conflict resolution (GMCR) is discussed for systematically investigating real world conflicts within a system of systems engineering outlook. This encompassing GMCR methodology has been constructed during the past three decades by the authors, their colleagues and students from many countries for addressing a rich range of conflict situations. GMCR can be used for studying large and small conflicts and includes methods for preference elicitation, preference uncertainty (unknown, fuzzy, grey numbers and probabilistic). Many kinds of definitions exist for possible human behavior under pure competition which can be transformed for utilization in coalition analysis. GMCR can handle emotions, attitudes, and misperceptions. Within inverse GMCR, one can calculate the preferences needed by decision makers (DMs) to reach a desirable equilibrium. Under behavioral GMCR one can ascertain the strategic thinking of DMs when the input and output are known. Decision support systems can be built for implementing the array of GMCR advancements. Future expansions of GMCR can be guided by key characteristics of actual disputes. Artificial intelligence (AI) GMCR is a promising subfield of study within GMCR.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".