Research on Consumer Credit with Game Theory: a Case of China’s Consumer Credit
Why this work is in the frame
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Bibliographic record
Abstract
This article introduces the development of China’s consumer credit, and analyses consumer credit behaviors with three game theory models, including fundamental game theory model of consumer credit behavior, improvement game theory model and repeated game model. Though analyzing these models, the article obtains the operating mechanism of consumer credit, and comes to conclusion that the complete sharing of consumer credit information in society is the technical support to develop consumer credit, and building Personal Credit Information Management System as soon as possible is the most urgent affair to the development of China’s consumer credit now. Key words: consumer credit, game theory, consumer credit information Resume: L’article presente le developpement de la consommation a credit de la Chine et analyse les comportements de consommation a credit avec trois modeles de la theorie du jeu, a savoir le modele de la theorie du jeu fondamental, le modele de la theorie du jeu ameliore et le modele de la theorie du jeu repete. A travers l’analyse de ces trois modeles, l’auteur trouve le mecanisme operatoire de la consommation a credit et tire la conclusion que le partage des informations sur la consommation a cedit dans la societe constitue le support technique du developpement de cette consommation et que l’etablissement du Systeme de Management de l’Information sur le Credit personnel le plus vite possible est l’affaire la plus urgente dans le developpement de la consommation a credit de la Chine. Mots-Cles: consommation a credit, theorie du jeu, information sur la consommation a credit
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it