Research on Competitive and Cooperative Strategies of Agricultural Product Marketing Based on Game Theory
Bibliographic record
Abstract
In the face of the traditional agricultural marketing model is difficult to continue the status quo, agricultural marketing competition -cooperation relationship for agricultural enterprises of commodity marketing and long-term development is also increasingly important.In this paper, game theory is introduced into the study of competition and cooperation strategy of agricultural products marketing, the strategic behavior of two agricultural products enterprises in the agricultural products industry cluster is constructed into the corresponding matrix, and evolutionary dynamic stability analysis is carried out to establish the replication dynamic equations and Jacobi matrix to solve the evolutionary stability strategy (ESS), so as to provide reference for the formulation of the competition and cooperation strategy of the enterprise's agricultural products marketing.Using simulation to explore the influencing factors of the evolutionary direction of the marketing competition and cooperation strategy of agricultural products enterprises.When the probability of winning the joint bidding is greater than 0.8, it will evolve into a cooperative strategy, and when it is less than 0.7, it will evolve into a competitive strategy, and with the increase of the allocation coefficient of the investment amount of the project construction, the agricultural products enterprise 1 and the agricultural products enterprise 2 will gradually shift from a competitive strategy to a cooperative strategy.The lower the cost allocation coefficient is, the higher the probability that enterprises will evolve to cooperative strategy.The increase of cooperative transaction cost then accelerates the evolution of enterprise 1 and enterprise 2 to competitive state.
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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) | 0.001 | 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 | 0.004 |
| 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".