Government‐industrial‐research cooperation in virtual water strategy: A multi‐agent evolutionary game analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The virtual water strategy (VWS) is an effective tool to balance regional water resource endowments and guarantee water supply security. However, because of self‐interested games around VWS (human decision bias), there is a need for methods to maintain reliable cooperation between governments, virtual water (VW) enterprises and research institutions. This study builds a multi‐agent evolutionary game model to analyse the relationship of players and their impacts on VWS through changing decision mechanisms and the paths to enhance their confidence in cooperation. Considering differences in initial willingness to cooperate and changing factors affecting payoffs, an evolutionary game can produce changing stable equilibriums or stable cooperations, even if some players are reluctant to cooperate. Therefore, to promote the development of VWS, a multistep support mechanism can be built for the VW industry, which fosters model enterprises and optimizes the cooperation framework to stimulate research innovations at scientific institutions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it