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Record W3132210013 · doi:10.1111/wej.12698

Game theory model for a virtual water strategy: Scenarios under rational and semirational game play

2021· article· en· W3132210013 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater and Environment Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsCanadian Museum of Nature
FundersGuizhou UniversityNational Natural Science Foundation of ChinaCanadian Museum of Nature
KeywordsGame theoryIncentiveComputer scienceResource (disambiguation)Mechanism (biology)Environmental economicsMicroeconomicsEconomics

Abstract

fetched live from OpenAlex

Abstract The virtual water strategy (VWS) provides a theoretically feasible method to transport virtual water (VW) and balance regional differences in water resource endowments. However, the practical implementation of VWS faces selective resistance due to the lack of research in motivation analysis across potential executor regions. Game theory provides a new approach to study the decision‐making mechanism and behavioural motivation in the implementation of VWS. This study builds some semiquantitative game models to analyse the regions’ potential acceptance of VWS in decision‐making, especially concerning economic development, water sustainability, and trade patterns. The result shows the factors like external incentives and stability of game can affect the payoffs and strategy selections on VWS in rational and semirational games. To achieve a mutual benefit equilibrium towards an effective VWS framework, improvement efforts could include mechanisms for rewards or punishments, promoting long‐term cooperation, and promulgation and education.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.556
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.179
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it