An Algorithm for Resilient Nash Equilibrium Seeking in the Partial Information Setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Current research in distributed Nash equilibrium (NE) seeking in the partial information setting assumes that information is exchanged between agents that are “truthful.” However, in general noncooperative games, agents may consider sending misinformation to neighboring agents with the goal of further reducing their cost. In addition, communication networks are vulnerable to attacks from agents outside the game as well as communication failures. In this article, we propose a distributed NE seeking algorithm that is robust against adversarial agents that transmit noise, random signals, constant singles, deceitful messages, as well as being resilient to external factors such as dropped communication, jammed signals, and man-in-the-middle attacks. The core issue that makes the problem challenging is that agents have no means of verifying if the information they receive is correct, i.e., there is no “ground truth.” To address this problem, we use an observation graph, which gives truthful action information, in conjunction with a communication graph, which gives (potentially incorrect) information. By filtering information obtained from these two graphs, we show that our algorithm is resilient against adversarial agents and converges to the NE.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it