Game and preferences analysis for virtual water strategy based on a Hotelling model
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 implementation of the virtual water strategy (VWS) transporting invisible water resources through product scheduling faces resistance due to limited reporting and understanding and the lack of motivation analysis for stakeholders. This study builds a semi‐quantitative Hotelling game model under different scenarios to analyse the influence of preference and material benefits on potential acceptance of VWS with policymakers and stakeholders. Equilibrium analyses of the game show that human preference can be as important as real benefits. With preference differences, it is hard to make all stakeholders accept or reject a VWS approach in achieving optimal results for environment and social welfare. To implement a sustainable VWS mode, modifying preferences through propaganda and education can be effective. The natural play of the game with modified preferences will ultimately favour a holistic VWS approach to responsible management. This model supports the effectiveness of game theory in the implementation of a VWS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it