On Stability and Convergence of Multi-Commodity Networks and Services
Bibliographic record
Abstract
Abstract—The rise of distributed services and user-driven networking concepts in recent years poses the critical question of stability. Can a system operating under non-cooperation and self-interest converge to a stable state? and how fast? The answers to these questions readily lend themselves to game theory analysis, and to the study of congestion games in particular. In the past, much work have been done on establishing the existence of pure Nash equilibria in congestion games, and has shown that finding a pure Nash equilibrium is PLS-complete [1] and hence convergence to a pure Nash equilibrium is very difficult (exponential time in worst case). Furthermore, much of the convergence analysis have been carried out on simple single-commodity game models. In this paper, we attempt to construct a more realistic multi-commodity congestion game model suited for distributed service and user-driven networking scenarios. We introduce the desirability of equilibrium concept that is helpful in determining whether a system state meets the quality requirements of the users and services. Desirability is an alternative concept to price of anarchy. In fact we show the desirability ratio is a special case of price of anarchy. We then define the α-threshold congestion game whose minimum potential state corresponds to a desirable equilibrium (if the system permits one) and we bound its convergence to polynomial time through game transformation. Finally, we present a mechanism for partial simultaneous moves. To the best of our knowledge, there has been no prior establishment of the desirability concept and no bound given on the convergence of asymmetric multi-commodity congestion games with exponential cost function. Index Terms—Game theory, stability, convergence I.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".