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Record W2008110601 · doi:10.3138/infor.51.3.103

A Basic Hierarchical Graph Model for Conflict Resolution with Application to Water Diversion Conflicts in China

2013· article· en· W2008110601 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueINFOR Information Systems and Operational Research · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicGame Theory and Applications
Canadian institutionsCentre for International Governance InnovationWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsGraphChinaComputer scienceComponent (thermodynamics)Water diversionOperations researchConflict resolutionGraph theoryManagement scienceMathematical optimizationTheoretical computer scienceMathematicsEconomicsPolitical scienceEnvironmental scienceWater resource management

Abstract

fetched live from OpenAlex

A basic hierarchical graph model with three decision makers is developed and used to analyze a water diversion conflict in China. This hierarchical graph model combines two component graph models. The theoretical framework of the combined model is constructed using the decision makers, states, moves, and preference structures from the component models. Theorems are developed to relate stable states in the hierarchical model to stable states in local graph models. This novel approach can avoid direct calculation for four hierarchical stabilities. This methodology is applied to a water diversion conflict in China, consisting of conflicts at two locations where it is proposed to divert water from the south to the north of the country. The analytical results show how decision makers can obtain strategic resolutions for the entire conflict.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.111
GPT teacher head0.390
Teacher spread0.279 · 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