A Study on Evolution Game of Accounts Receivable Pledge Financing in Supply Chain Finance Model
Bibliographic record
Abstract
In practice, due to information asymmetry and the bounded rationality of game participants, most of the actions taken by game participants are irrational. The evolutionary game theory is based on bounded rationality and looks at the adjustment process of group behavior through the perspective of system theory. Therefore, this paper will use the idea of evolutionary game to discuss the issue of SMEs' accounts receivable pledge financing under the supply chain finance model. This paper builds the model on the basis that both sides of the game are bounded rationality. In real life, the behavior of individuals tends to be more bounded rationality. By constructing an evolutionary game model under bounded rationality, we can see that the final evolution result between banks and loan companies is related to many factors. From the phase diagram of the evolutionary game, it can be seen that the area on both sides of the dotted line of the phase diagram is mainly determined by the profit matrix, but the direction of the final evolution is mainly determined by the initial state of the game.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".