Stochastic Adaptive Nash Certainty Equivalence Control: Self-Identification Case
Bibliographic record
Abstract
For noncooperative games the Nash Certainty Equivalence (NCE), or Mean Field (MF) methodology de- veloped in previous work provides decentralized strategies which asymptotically yield Nash equilibria. The NCE (MF) control laws use only the local information of each agent on its own state evolution and knowledge of its own dynamical parameters, while the behaviour of the mass is precomputable from knowledge of the distribution of dynamical parameters throughout the mass population. Relaxing the a priori information condition introduces the methods of parameter estimation and stochastic adaptive con- trol (SAC) into MF control theory. In particular one may consider incrementally the problems where the agents must estimate: (i) its own dynamical parameters, (ii) the distribution of the population's dynamical parameters (1), and (iii) the distribution of the population's cost function parameters (2). In this paper we treat the first problem. Each agent estimates its own dynamical parameters via the recursive weighted least squares (RWLS) algorithm. Under reasonable conditions on the population dynamical parameter distribution, we establish: (i) the strong consistency of the self- parameter estimates; and that (ii) all agent systems are long run average L 2 stable; (iii) the set of controls yields a (strong) -Nash equilibrium for all ; and (iv) in the population limit the long run average cost obtained is equal to the non-adaptive long run average cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".