GNE seeking in games with passive dynamic agents via inexact-penalty methods
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Bibliographic record
Abstract
In this paper, we consider a network of autonomous agents with passive linear time-invariant dynamics involved in a game with coupled constraints. In such networked scenarios, agents have to make decisions compatible with seeking a generalized Nash equilibrium (GNE), while using networked information and satisfying the constraints. Existing methods are developed for multi-integrator agents only and furthermore, ensure the satisfaction of coupled constraints in steady-state only. We propose an inexact-penalty dynamics for passive LTI agents and show that it converges to an ε -GNE while ensuring the coupled constraints are met throughout the evolution of the agents' dynamics, not only in steady-state. Our scheme is developed for both the full-decision information setting and the partial-decision one. In the partial-information setting, each agent makes its decision based on a dynamic estimate of the others' states, updated by local communication with its neighbours, which offsets the lack of global information. Applications to optical networks are provided.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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