On the Existence of Optimal Taxes for Network Congestion Games with Heterogeneous Users
Bibliographic record
Abstract
Abstract. We consider network congestion games in which a finite num-ber of non-cooperative users select paths. The aim is to mitigate the inefficiency caused by the selfish users by introducing taxes on the net-work edges. A tax vector is strongly (weakly)-optimal if all (at least one of) the equilibria in the resulting game minimize(s) the total latency. The issue of designing optimal tax vectors for selfish routing games has been studied extensively in the literature. We study for the first time taxation for networks with atomic users which have unsplittable traffic demands and are heterogeneous, i.e., have different sensitivities to taxes. On the positive side, we show the existence of weakly-optimal taxes for single-source network games. On the negative side, we show that the cases of homogeneous and heterogeneous users differ sharply as far as the existence of strongly-optimal taxes is concerned: there are parallel-link games with linear latencies and heterogeneous users that do not admit strongly-optimal taxes. 1
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".