Analysis of Regional Tourism Strategy Cooperation Based on the Evolutionary Game Theory
Bibliographic record
Abstract
This paper uses evolutionary game theory to analyse regional tourism cooperation. Asymmetricevolutionary game model is established to explore the intrinsic motivation of regional tourismcooperation, and we via rigorous mathematical reasoning to prove a series of proposition. At the same time, through numerical simulation on each proposition by the MATLAB software, it vividly reflects the evolution process of the regional tourism cooperation. The results show that, first, it is necessary to cooperate, which is the inevitable trend in the development; Second, we shall specify the necessary condition to maintain cooperation, when the no-cooperative party can obtain additional benefit of freeriding from the cooperation party, and the additional benefit is greater than the benefit from bilateral cooperation, cooperation will ultimately fail; third, we must focus on the sufficient conditions to guarantee cooperation, which mainly reflected on the utilization of shared resources, cooperation cost, the benefit of “free-riding” etc.. In view of the results, we propose some suggestions to promote regional tourism cooperation from the regulations of regional tourism cooperation, government and the utilization of shared resources.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".