Distributed Nash equilibrium seeking resilient to adversaries
Bibliographic record
Abstract
Most research in distributed Nash Equilibrium (NE) seeking assumes that agents communicate truthfully. However, in general noncooperative games agents may have the incentive to send misinformation to neighbouring agents with the goal of minimizing their own costs. Furthermore, such settings can also be susceptible to communication failures and attacks from agents outside the game. In this paper, we design a NE seeking algorithm that is resilient against malicious agents and communication tampering/failures. The problem is challenging because adversarial agents may be indistinguishable from a truthful agent with a modified (and valid) cost function. The core issue is that agents lack any means of verifying if the information they receive is truthful, i.e. there is no "ground truth". To address this problem, we make use of an observation graph in addition to a communication graph, as well as pruning of extreme messages. Under the assumption that the number of adversaries/malicious agents does not exceed the number of truthful ones, we show that our algorithm is resilient against adversarial agents and converges to the Nash equilibrium.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".