Synthetic multi-criteria decision analysis (S-MCDA): A new framework for participatory transportation planning
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".