Bibliographic record
Abstract
We consider a priority-based selfish routing model, where agents may have different priorities on a link.An agent with a higher priority on a link can traverse it with a smaller delay or cost than an agent with lower priority.This general framework can be used to model a number of different problems.The structural properties that lead to inefficiencies in routing choices appear different in this priority-based model compared to the classical model.In particular, in parallel link networks with nonatomic agents, the price of anarchy is exactly one in the priority-based model; that is, selfish behaviour leads to optimal routings.In contrast, in the standard model the worst possible price of anarchy can be achieved in a simple two-link network.For multi-commodity networks, selfish routing does lead to inefficiencies in the priority-based model.We present tight bounds on the price of anarchy for such networks.Specifically, in the nonatomic case the worst-case price of anarchy is exactly (d + 1) d+1 for polynomial latency functions of degree d (hence 4 for linear cost functions).For atomic games, the worst-case price of anarchy is exactly 3 + 2 √ 2 in the weighted case, and exactly 17/3 in the unweighted case.An upper bound of O(2 d d d ) is also shown for polynomial cost functions in the atomic case, although this is not tight.Our framework (and results) also generalise to include models similar to congestion games.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".