ε-Nash equilibria for partially observed LQG mean field games with major agent: Partial observations by all agents
Bibliographic record
Abstract
LQG mean field systems with a major agent (i.e. non-asymptotically vanishing as the population size goes to infinity) and a population of minor agents (i.e. individually asymptotically negligible) are studied in (Huang, 2010) and (Nguyen and Huang, 2012). Due to presence of the major agent, the mean field becomes stochastic in contrast to the case with purely minor agents where mean field is deterministic (Huang et al 2007). In (Caines and Kizilkale, 2013, 2014, ŗen and Caines 2013, 2014), it is assumed the major agent's state is partially observed by each minor agent, and the major agent completely observes its own state. Accordingly, each minor agent can recursively estimate the major agent's state, compute the system's mean field and thence generate the feedback control which yields ε-Nash equilibrium property. This paper investigates the problem of estimation and control for an LQG mean field system in which both the major agent and the minor agents partially observe the major agent's state. The existence of ε-Nash equilibria together with the individual agents' control laws yielding the equilibria are established wherein each agent recursively generates estimates of the major agent's state and hence generates a version of the system's mean field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".