Dynamic Nash Equilibrium Seeking for Higher-Order Integrators in Networks
Bibliographic record
Abstract
In this paper we consider a set of heterogeneous agents modelled as higher-order integrators, playing a game over a network. In such networked scenarios, agents have to make decisions compatible with seeking a Nash equilibrium, while using partial-networked information, and possibly rejecting disturbances. We propose dynamic agent decision-making based on gradient-play with an additional stabilizing component for the higher-order dynamics. 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. When external disturbances are present, the agent decision dynamics is augmented with an internal-model component, in the form of a reduced-order observer for the disturbance. We show convergence of agents' dynamics to the Nash equilibrium, irrespective of disturbances. Our proofs leverage input-to-state stability under strong monotonicity of the pseudo-gradient and Lipschitz continuity of extended pseudo-gradient. Applications to mobile robots in sensor networks are provided.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".