Inter-Session Network Coding with Strategic Users: A Game-Theoretic Analysis of the Butterfly Network
Bibliographic record
Abstract
We analyze inter-session network coding in a wired network using game theory. We assume that users are selfish and act as strategic players to maximize their own utility, which leads to a resource allocation game among users. In particular, we study a butterfly network, where a bottleneck link is shared by network coding and routing flows. We assume that network coding is performed using pairwise XOR operations. We prove the existence of Nash equilibrium for a wide range of utility functions. We also show that the number of Nash equilibria can be large (even infinite) for certain choices of parameters. This is in sharp contrast to a similar game setting with traditional packet forwarding, where the Nash equilibrium is always unique. We characterize the worst-case efficiency bound, i.e., the Price-of-Anarchy (PoA), compared to an optimal and cooperative network design. We show that by using a discriminatory pricing scheme which charges encoded and forwarded packets differently, we can improve the PoA in comparison with the case where a single pricing scheme is used. However, even when a discriminatory pricing scheme is used, the PoA is still worse than for the case when network coding is not applied. This implies that, although inter-session network coding can improve performance compared to routing, it is much more sensitive to users' strategic behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 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".