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Record W4394911997 · doi:10.1287/mnsc.2023.00383

Strategy-Proof Multi-Issue Mediation: An Application to Online Dispute Resolution

2024· article· en· W4394911997 on OpenAlexaff
Onur Kesten, Selçuk Özyurt

Bibliographic record

VenueManagement Science · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsYork University
Fundersnot available
KeywordsMediationAlternative dispute resolutionProof of conceptDispute resolutionComputer scienceResolution (logic)Law and economicsBusinessProcess managementPolitical scienceEconomicsLawArtificial intelligence

Abstract

fetched live from OpenAlex

Mediation (assisted negotiation) is the preferred alternative dispute resolution approach that has given rise to a multibillion-dollar industry worldwide. Online dispute resolution (ODR) providers rely heavily on mechanized e-negotiation systems. Inspired by their structured negotiation systems, we follow a market design approach and develop a tractable framework in search of strategy-proof, efficient, and individually rational mediation mechanisms. We characterize the full family of such mechanisms and the domains of preferences in which they exist. A necessary and sufficient condition for the existence of such protocols is the so-called quid pro quo property; a weak condition that formulates preferences for compromise solutions. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. Funding: S. Özyurt is grateful for the support he received from the Social Sciences and Humanities Research Council and the European Commission [Grant 659780]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.00383 .

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.037
GPT teacher head0.270
Teacher spread0.233 · 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.

Study designTheoretical or conceptual
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

Citations2
Published2024
Admission routes1
Has abstractyes

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