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Record W4393859930 · doi:10.1139/cjce-2023-0437

The behavioral strategies of multiple stakeholders in the NIMBY facility public-private partnership project: a tripartite evolutionary game analysis based on prospect theory

2024· article· en· W4393859930 on OpenAlexvenueno aff
Xiaotong Cheng, Min Cheng, Yaqun Liu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEvolutionary game theoryGeneral partnershipNIMBYPrivate sectorGame theoryPublic–private partnershipBusinessPublic sectorProspect theoryYardEconomicsMicroeconomicsPublic economicsMarketingEngineeringFinanceEconomic growth

Abstract

fetched live from OpenAlex

In this study, evolutionary game method, prospect theory, and system dynamics (SD) method are combined to analyze the key stakeholders’ behaviors in the public-private partnership project of not-in-my-back-yard facility. First, the interaction behavior of the public sector, the private sector, and the public and their equilibrium state was analyzed based on the evolutionary game method and prospect theory. Then, the SD method was used to simulate and analyze the impact of different variables on the behavior of the three stakeholders. The results show that the greater the perceived cost difference between the active and negative behavior among the three stakeholders, the more likely they are to take negative behavior. The private sector tends to act opportunistically under low-risk loss situations. Dynamic rewards are more likely to incentivize the public to supervise than high rewards. Some recommendations to promote active behavioral interactions and cooperation among stakeholders were presented accordingly.

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.

How this classification was reachedexpand

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)0.001
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: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.042
GPT teacher head0.227
Teacher spread0.185 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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