Distributed Nash equilibrium seeking resilient to adversaries
Why this work is in the frame
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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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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