Efficiency of Market-Based Resource Allocation among Many Participants
Bibliographic record
Abstract
Market mechanisms have been suggested in the last few years as a tool for allocating shared networks resources among several competing users. In this paper, we consider the efficiency loss of such mechanisms in the presence of a large number of users. We model the user interactions as a game with a heterogeneous population of players characterized by random utility functions. If the utility functions are bounded, then the non-cooperative equilibrium are nearly as efficient as the social optimum with high probability when the number of users is large. This efficiency result holds for a single link with a fixed or an increasing capacity. Using a standard probabilistic analysis, we show that the efficiency loss incurred by the market mechanism decreases almost exponentially in the number of users. If, however, the utility functions are not bounded, then the loss of efficiency does not converge to zero. We also provide results for networks by sampling the users at random based on their paths.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".