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Record W2132897157 · doi:10.1287/mnsc.1070.0736

Final-Offer Arbitration and Risk Aversion in Bargaining

2007· article· en· W2132897157 on OpenAlexaff
Eran Hanany, D. Marc Kilgour, Yigal Gerchak

Bibliographic record

VenueManagement Science · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsNegotiationArbitrationBargaining problemOutcome (game theory)StipulationSalaryEconomicsRisk aversion (psychology)MicroeconomicsNash equilibriumLeagueWage bargainingWorkgroupMathematical economicsWageExpected utility hypothesisComputer scienceLabour economicsLawPolitical science

Abstract

fetched live from OpenAlex

Negotiations are often conducted under the stipulation that an impasse is to be resolved using final-offer arbitration (FOA). In fact, FOA frequently is not needed; in Major League Baseball, for instance, more than 80% of the salary negotiations that could go to arbitration instead reach a bargained agreement. We show that the risk aversion of at least one side explains this phenomenon. We then model pay negotiation in baseball by applying a bargaining solution with a variable disagreement outcome representing FOA, studying the existence of pure Nash equilibrium initial offers and their effects on the player's eventual pay, and considering the Nash solution as a special case.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.029
GPT teacher head0.220
Teacher spread0.191 · 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 designObservational
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

Citations14
Published2007
Admission routes1
Has abstractyes

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