A Game-Theoretic Analysis of Inter-Session Network Coding
Bibliographic record
Abstract
A common assumption in the network coding literature is that the users are cooperative and will not pursue their own interests. However, this assumption can be violated in practice. In this paper, we analyze inter-session network coding in a wired network, assuming that the users are selfish and act as strategic players to maximize their own utility. We prove the existence of Nash equilibria for a wide range of utility functions. The number of Nash equilibria can be large (even infinite) under certain conditions, which is in sharp contrast to a similar game setting with traditional packet forwarding. We then characterize the worst-case efficiency bounds, i.e., the price-of-anarchy (PoA), compared to an optimal and cooperative network design. We show that by using a novel discriminatory pricing scheme that charges encoded and forwarded packets differently, we can improve PoA in comparison with the case where a single pricing scheme is being used. However, PoA is still worse than the case when network coding is not applied. This implies that inter-session network coding is more sensitive to strategic behavior. For example, for the case where only two network coding flows share a single bottleneck link, the efficiency at certain Nash equilibria can be as low as 48%. These results generalize the well-known result of guaranteed 67% efficiency bounds shown by Johari and Tsitsiklis for traditional packet forwarding networks.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".