Exemples de restauration d’unicité et de sélection d’équilibres dans les jeux à champ moyen
Bibliographic record
Abstract
The purpose of this thesis is to present several results on the restoration of uniqueness and selection of equilibria when uniqueness fails in mean field games. The theory of mean field games was initiated in the 2000s by two groups of researchers, Lasry and Lions in France, and Huang, Caines, and Malhamé in Canada. The aim of this theory is to describe the Nash equilibria in stochastic differential games involving a large number of players interacting with each other through their common empirical measure, under sufficient symmetry hypothesis. If the existence of equilibria in mean field games is now well understood, uniqueness remains known in a very limited number of cases. In this respect, the most well-known condition is the monotony hypothesis, due to Lasry and Lions. In this thesis, we demonstrate that for a certain class of mean field games, uniqueness can be restored by means of a random and common forcing, acting on all the players. Such a forcing is called “common noise”. We also show that in some cases it is possible to select equilibria in the absence of common noise by letting the common noise tend towards zero. Finally, we show how these results apply to “principal-agent” .problems, with a large number of interacting agents.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".