Bibliographic record
Abstract
We consider a forwarding game on directed graphs where nodes need to send certain amount of flow (packets) to specific destinations, possibly through several relay nodes. All nodes in the network act selfishly and will forward packets only if it is to their benefit. The model assumes that each node receives some utility from sending it flow to the predetermined destinations and from receiving flow. However each node has to decide whether to relay flow as an intermediate node from other sources, as relaying has an associated cost. This model assumes that there is no payment scheme. Somewhat surprisingly, this game has possibly several strategies that allow a significant amount of the flow to be routed while all nodes have a positive outcome, which suggest that in this model the nodes have indeed incentives to relay flow even if payments are not explicitly allocated. Although previous theoretical work establishes the existence of these strategies (Nash equilibrium solutions), it is not known how often networks have such solutions, and what percentage of flow is actually relayed through the network. In this work we simplify the original network model, and provide the first experimental evaluation of these equilibria for various classes of graphs. We provide clear evidence that these equilibrium solutions are indeed significant and establish how these equilibria depend on various properties of the network such as average degrees and flow demand density.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".