Bibliographic record
Abstract
In this thesis, we investigate methods of designing distributed (generalized) Nash equilibrium (GNE) seeking algorithms in continuous-time for agents with inherent dynamics. In real-world applications, the action of each agent may correspond to a physical quantity, that is actuated through a control input. In such settings, the algorithms considered take the form of distributed, dynamic feedback controllers with networked communication that seek to drive the action to the (G)NE in steady-state. The specific contributions of the thesis take two forms. First, we propose a general framework for designing distributed Nash equilibrium (NE) seeking controllers for decoupled LTI agents. Using this methodology, we show that the problem is reduced to the design of a set of decentralized stabilizing controllers. We investigate various methods of designing these controllers, first for quadratic games using LTI control theory and diagonal stability theory and then for non-quadratic games using passivity and H-infinity control theory. Second, we consider designing distributed GNE seeking feedbacks for dynamic agents in games with coupled constraints. Current methods can only ensure constraint satisfaction in steady-state. In contrast, we propose an inexact penalty method using a barrier function for agents with equilibrium-independent passive dynamics. Initially, we show that with fixed barrier function these dynamics converge to a suboptimal epsilon-GNE while satisfying the constraints for all time, not only in steady-state. Then, we consider allowing the log-barrier function to vary in time in order to achieve exact convergence to the variational GNE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".