Stability of time-dependent elastic Nash equilibrium in oligopolistic markets under perturbation
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Bibliographic record
Abstract
In this paper, we consider time-dependent elastic Nash equilibria in oligopolistic markets.Our analysis underlines the perturbation phenomena that should be taken into account in the study of such equilibrium problems.Precisely, there are both internal and external sources generating variations at any time of the commodity shipment between the supply market and the demand market.This also affects, in principle, commodity outputs as well as the commodity at demand markets.Accordingly, the three costs of production, the storage and the transportation, respectively, involved in a such model are perturbed by the same and/or another perturbation.Then, in this work, we first fix the parametric market equilibrium model for our problem, which we characterize by a time-dependent quasi-variational inequality problem what enables us to state and prove our main result on the stability of the parametric equilibrium commodity shipment in question.Our key tools make recourse to recent developments of quasi-equilibrium problems as well as the qualitative stability of fixed points of set-valued maps.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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