Research on Consumer Credit with Game Theory: a Case of China’s Consumer Credit
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
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".