Research on competition and cooperation in the two level multi agent supply chain of China's new energy vehicle industry based on differential game theory
Bibliographic record
Abstract
Based on differential game theory, this article studies the competition and cooperation in the supply chain system of the new energy vehicle industry. It constructs profit functions for new energy vehicle manufacturers and core component suppliers in two different situations: competitive decision-making and cooperative decision-making. The optimal factor values and profit values of each subject are obtained, and case analysis is conducted. Research has found that: (1) Whether in competitive or cooperative decision-making situations, the optimal factor values of each subject are influenced by their respective costs and show an increasing trend. (2) By comparison, it is found that the optimal profit of new energy vehicle manufacturers under cooperative decision-making and the overall optimal profit of the new energy vehicle industry supply chain system are better than those under competitive decision-making. (3) The profit growth rate of core component suppliers of new energy vehicles and the supply chain system of the new energy vehicle industry under cooperative decision-making is significantly faster than that under competitive decision-making.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".