Bibliographic record
Abstract
For noncooperative games the mean field (MF) methodology provides decentralized strategies which yield Nash equilibria for large population systems in the asymptotic limit of an infinite (mass) population. The MF control laws use only the local information of each agent on its own state and own dynamical parameters, while the mass effect is calculated offline using the distribution function of (i) the population's dynamical parameters, and (ii) the population's cost function parameters, for the infinite population case. These laws yield approximate equilibria when applied in the finite population. In this paper, these a priori information conditions are relaxed, and incrementally the cases are considered where, first, the agents estimate their own dynamical parameters, and, second, estimate the distribution parameter in (i) and (ii) above. An MF stochastic adaptive control (SAC) law in which each agent observes a random subset of the population of agents is specified, where the ratio of the cardinality of the observed set to that of the number of agents decays to zero as the population size tends to infinity. Each agent estimates its own dynamical parameters via the recursive weighted least squares (RWLS) algorithm and the distribution of the population's dynamical parameters via maximum likelihood estimation (MLE). Under reasonable conditions on the population dynamical parameter distribution, the MF-SAC Law applied by each agent results in (i) the strong consistency of the self parameter estimates and the strong consistency of the population distribution function parameters; (ii) the long run average stability of all agent systems; (iii) a (strong) e-Nash equilibrium for the population of agents; and (iv) the a.s. equality of the long run average cost and the non-adaptive cost in the population limit.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".