Evaluating transportation regulation using behavioral experimentation: The maximum revenue entitlement (MRE) policy for grain transportation in Canada
Bibliographic record
Abstract
The goal of this research is to gain insight into hitherto poorly understood behavioral consequences associated with the implementation of the Maximum Revenue Entitlement (or MRE) policy in Canadian rail, a regulation applied specifically to grain movement. Since a duopoly rail market serves the vast Canadian grain handling sector, the MRE was implemented in 2000 to help regulate transportation rates on moving grain. Despite its longevity as a regulatory policy, no analysis has ever been undertaken to assess how behavioral incentives attributable to the MRE might affect relevant stakeholders in the Canadian grain supply chain. Using a cross-disciplinary approach, we examine the MRE analytically prior to developing behavioral experiments designed to emulate this regulated supply chain. First, analytics indicate that as designed the MRE is biased towards longer distance movements. Next, our baseline experiment showed that optimizing behavior for all participants under the MRE is not an easy task, but more careful decision-making behavior occurred with a greater MRE penalty. However, the identified analytic bias over distance was not consistently exploited by the experimental participants. Overall, the experimental analysis provided additional clarity about unforeseen issues with the MRE, while also showing that the MRE policy is in need of re-evaluation to ensure that this crucial supply chain remains economically sustainable for all participants.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".