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Record W4236544481 · doi:10.22215/etd/2017-11854

Mean Field Games with Poisson Point Processes and Impulse Control

2017· dissertation· en· W4236544481 on OpenAlexaff
Mengjie Zhou

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsCarleton University
Fundersnot available
KeywordsImpulse controlMathematicsPoisson distributionImpulse (physics)Markov processApplied mathematicsCompound Poisson processImpulse responseStationary pointStatistical physicsStationary distributionMean field theoryField (mathematics)Mathematical optimizationMathematical analysisMarkov chainPoisson processStatisticsPhysicsPure mathematicsClassical mechanics

Abstract

fetched live from OpenAlex

This thesis considers mean field games in a continuous time competitive Markov decision process framework.Each player's state has pure jumps modeled by a self-weighted compound Poisson process subject to impulse control.We focus on analyzing the steady-state (or stationary) equation system of the mean field game.The best response is determined as a threshold policy and the stationary distribution of the state is derived in terms of the threshold value.The numerical solution of the equation system is developed.We further generalize the model to an unbounded state space.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.002
GPT teacher head0.198
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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