The Graph Model for Conflict Resolution and Decision Support
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 | 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