Sensitivity analysis for multi-attribute project selection problems
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Bibliographic record
Abstract
The selection of projects from a large list of possibilities is a common problem in Civil and Environmental Engineering. Optimization models based on utility assessments and expert opinions tend to be neglected by decision-makers because of uncertainty in the inputs. Here we develop a multi-attribute optimization model for project selection problems that is based on a combination of multi-attribute utility theory, mixed-integer optimization, and statistical analysis. A series of procedures for conducting sensitivity analyses for both discrete and continuous parameter variations is provided. To demonstrate the model we use the example of selecting a portfolio of projects to fund within the New Zealand Department of Conservation (DoC). The results highlight the sensitivity of project selection to attribute weights, and the difficulty in removing non-critical parameters from an analysis when wide ranges in parameters are considered. The methods developed here will help decision-makers examine the robustness of the optimal solution to parameters, and help them focus improvements to their decision support on the most critical parameters.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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