Representing ordinal preferences in the decision support system GMCR II
Bibliographic record
Abstract
Preference information about states, or possible scenarios, is required in modeling multiple-participant decision processes. In the decision support system GMCR II, a flexible methodology is presented for conveniently eliciting a decision maker's relative preferences. More specifically, when states are defined in terms of discrete option choices, three techniques are available for ordering the states from most to least preferred, with ties permitted. One method is option weighting, in which weights are assigned to each option choice, and total weights used to determine an ordering of states. A second technique is to employ an option prioritizing scheme, based upon a set of lexicographic statements. Subsequently, one can rank the states manually using a process called fine tuning. When the number of states is not too large, one may wish to order them directly and thereby skip the option weighting and option prioritizing procedures. Application of these approaches to obtain and represent ordinal preference information allows GMCR II to model real-world conflicts expeditiously, and analyze them effectively.
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How this classification was reachedexpand
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".