Synthetic multi-criteria decision analysis (S-MCDA): A new framework for participatory transportation planning
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
Participatory multi-criteria decision analysis plays a vital role in transportation planning by integrating diverse stakeholder views and balancing conflicting objectives. However, it faces high costs, time demands, and coordination difficulties. This paper introduces the Synthetic Multi-Criteria Decision Analysis (S-MCDA) framework, which utilizes large language models to generate synthetic actors to support participatory decision-making in transportation planning. A literature review combining bibliometric and content analysis highlights current methods across logistics, road, rail, maritime, and transit sectors. Based on these findings, the S-MCDA framework addresses stakeholder complexity and streamlines tasks like structuring analyses, eliciting preferences, and evaluating results. While the framework has the potentially to significantly improve consistency and decision quality, it raises concerns regarding computation, ethics, and AI over-reliance. Thus, the paper offers best practices for managing data quality, reducing bias, ensuring human oversight, and promoting transparency. Future research should further explore the use of synthetic agents to support collaborative decision-making in complex transport systems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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