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Combined strategic and tactical negotiation methodology for resolving complex brownfield conflicts

2010· article· en· W1965612845 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.

Bibliographic record

VenuePesquisa Operacional · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBrownfieldNegotiationRedevelopmentComputer scienceOutcome (game theory)Operations researchManagement scienceSet (abstract data type)Conflict resolutionProcess managementBusinessMicroeconomicsEconomicsEngineeringPolitical scienceCivil engineering

Abstract

fetched live from OpenAlex

An innovative negotiation methodology for strategic and tactical decision making is proposed for resolving conflicts in brownfield redevelopment. At the strategic level, the Graph Model for Conflict Resolution is systematically employed for determining a potential overall agreement, or set of resolutions, that is politically possible given the competing interests of the decision makers involved in a brownfield redevelopment project. At the tactical level, a possible strategic solution can be studied in depth using utility theory to determine trade-offs or concessions needed to reach a mutually acceptable detailed solution. Also, the proposed negotiation methodology can take into account the attitudes of negotiators and investigates the impact of the negotiators' attitudes on the outcome of negotiations at both levels of negotiation. The design of a negotiation decision support system is put forward to allow the proposed negotiation methodology to be conveniently applied to actual disputes.

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.507
GPT teacher head0.486
Teacher spread0.021 · 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