Mean-Field Control for Stochastic Delay Systems via Static Output Feedback Strategy
Bibliographic record
Abstract
In this paper, we consider mean-field control based on the static output feedback (SOF) strategy for stochastic delay systems. First, we define a stabilization problem via SOF gains in block-diagonal forms for systems with a single player, and then solve the problem of minimizing the upper bound of the cost function by cost-guaranteed cost control theory. For this problem, the necessary conditions for the sub-optimality are established using stochastic large-scale matrix equations. The obtained preliminary results are then used to study Pareto optimal strategies in cooperative games, for mean-field stochastic systems involving a large number of players. The primary contribution of this study is the derivation of a design method for decentralized strategies. Furthermore, a new low-order computational algorithm based on Newton's method is developed to obtain the decentralized strategy set. The cost degradation of the proposed decentralized SOF strategy set is then estimated. Finally, a simple numerical example is presented to demonstrate the usefulness and effectiveness of the proposed method. As a result, it is determined that the decentralized SOF strategy works well even when the number of players goes to infinity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".