GMFG critical nodes for control affine systems with exponentiated costs
Bibliographic record
Abstract
Graphon Mean Field Games (GMFGs) (Caines and Huang, 2021) constitute generalizations of Mean Field Games (MFGs) for which the agents form subpopulations associated with the nodes of large graphs, where infinite cardinality graph node and edge limits are considered with limit graphons g ( α , β ) , ( α , β ) ∈ [ 0 , 1 ] × [ 0 , 1 ] . The work in (Foguen-Tchuendom et al., (2021, 2022) [10],[11]) analyzed the stationarity of Nash equilibrium values with respect to node location for large populations of non-cooperative agents with linear dynamics on large graphs together with their limit graphons. The analysis in (Foguen-Tchuendom et al.,(2021, 2022) [10],[11]) is extended in this investigation to agent systems lying in the class of control affine non-linear systems (see Isidori (1985)). Specifically, control affine GMFG systems in an infinite network are treated where (i) at each node α ∈ [ 0 , 1 ] the drift of each generic agent system is affine in the control function, and (ii) the running costs at each node α are exponentiated negative inverse quadratic functions of the difference between a generic state and the graphon g weighted local mean field Z α , g , which involves ≔ μ G ≔ { μ β , β ∈ [ 0 , 1 ] } representing agent state distributions at different nodes. The GMFG equation system is proved to have a unique solution under a contraction condition, and the main result is that Nash equilibrium values V α are stationary with respect to the node location α ∈ [ 0 , 1 ] if the corresponding graphon weighted local mean field Z α , g is stationary with respect to node location; the converse also holds if the model only has cost-coupling.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".