Nash Equilibrium Seeking Over Undirected Graphs Via Multi-Agent Agreement
Bibliographic record
Abstract
We consider the problem of distributed Nash equilibrium seeking over networks.\nIn this setting, agents have limited information about the other players' actions and are forced to communicate with neighbouring agents.\nWe start with a continuous-time gradient-play dynamics, with perfect information, under a strictly monotone pseudo-gradient assumption.\nIn the partial information case we modify the gradient-play dynamics between players by expanding the action space.\nWe propose an augmented gradient-play dynamics in which players can only communicate locally with their neighbours to compute an estimate of the other players' actions.\nWe derive new dynamics based on the reformulation of the problem as a multi-agent coordination problem, over an undirected graph.\nWe exploit the incremental passivity properties in the dynamics and show that a Laplacian feedback can be designed using relative estimates of their neighbours.\nWe highlight that there is a trade-off between properties of the game and the communication graph.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 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".