Consequence modelling based on stated preferences
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
AbstractA decision making framework is developed that includes several decision makers (instead of just one) having different preferences and value systems. The information provided by the varying opinions of decision makers can be used to optimise our own decision making. To achieve this, likelihood functions are developed for stated preferences among both discrete and continuous alternatives, and stated preference rankings of alternatives. The specific case is considered of optimisation of the lifecycle utility of a structural system subject to consequences of failure proportional to the intensity of hazards exceeding a variable threshold, and to follow-up consequencesKey Words: Preference modellingutilityconsequencesBayesian decision makingexpert solicitation. Additional informationNotes on contributorsM A MaesMarc MaesMarc A Maes is a professor in civil engineering, who conducts research in risk analysis, probabilistic modelling and engineering decision making. He is active in numerous international organisations and code committees, and often acts as a specialist consultant to industry.M H FaberMichael FaberMichael H Faber is a professor in civil engineering and is active in research related to rational decision making in civil engineering problems subject to uncertainty. This includes all aspects of probabilistic modelling, risk based optimal design, experiment planning, maintenance planning and life cycle analysis.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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