Recursive estimation of common partially observed disturbances in MFG systems with application to large scale power markets
Bibliographic record
Abstract
Power markets are modelled as dynamic large population games where suppliers and consumers submit their bids in real-time. The agents are coupled in their dynamics and cost functions through the price process. Here, a common unpredictable partially observed major agent is added to the system which represents common unpredictable disturbance factors (e.g. wind) and exogenous market factors (e.g. competing energy resource prices), etc. In previous work, the Mean Field Game (MFG) methodology was used to study the limit (Le., infinite population) behaviour of large population market systems without a major agent; this results in a decentralized algorithm where agents submit their bids solely using statistical information on the dynamics of the entire population. When a major agent is absent, the system exhibits the standard counter intuitive property of MFG solutions that agents need not observe the behaviour (i.e., inputs and state trajectories, market price evolution, etc.) of any other agent (individually or collectively) in order that simple decentralized control actions achieve a mass ϵ-Nash equilibrium (with ϵ vanishing as the population goes to infinity) and individual L2stability. The contribution of this paper is the extension of the MFG theory to cover the addition of a major agent to the power market problem. In general, the addition of a major agent in the MFG framework makes the mean field stochastic in contrast to the situation with purely minor agents where the mean field is deterministic. In the general situation of sporadic noisy observations of the mean field and the state of the major agent, the extended MFG theory (with estimation of the mean field and the major agent state) yields simple decentralized control laws which achieve a mass ϵ-Nash equilibrium (with ϵ vanishing as the population goes to infinity) and individual L2stability. In this paper, this is carried out for the MFG formulation of the power market problem in order to fit the situation where sporadic noisy observations of the state of the major agent and of the market price are available for recursive mean field state estimation.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".