MinMax Mean-Field Team Approach for a Leader-Follower Network: A\n Saddle-Point Strategy
Bibliographic record
Abstract
This paper investigates a soft-constrained MinMax control problem of a\nleader-follower network. The network consists of one leader and an arbitrary\nnumber of followers that wish to reach consensus with minimum energy\nconsumption in the presence of external disturbances. The leader and followers\nare coupled in the dynamics and cost function. Two non-classical information\nstructures are considered: mean-field sharing and intermittent mean-field\nsharing, where the mean-field refers to the aggregate state of the followers.\nIn mean-field sharing, every follower observes its local state, the state of\nthe leader and the mean field while in the intermittent mean-field sharing, the\nmean-field is only observed at some (possibly no) time instants. A social\nwelfare cost function is defined, and it is shown that a unique saddle-point\nstrategy exists which minimizes the worst-case value of the cost function under\nmean-field sharing information structure. The solution is obtained by two\nscalable Riccati equations, which depend on a prescribed attenuation parameter,\nserving as a robustness factor. For the intermittent mean-field sharing\ninformation structure, an approximate saddle-point strategy is proposed, and\nits converges to the saddle-point is analyzed. Two numerical examples are\nprovided to demonstrate the efficacy of the obtained results.\n
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".