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Record W2916607818 · doi:10.3982/te3647

Gradual pairwise comparison and stochastic choice

2020· article· en· W2916607818 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

VenueTheoretical Economics · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsMcGill University
Fundersnot available
KeywordsPairwise comparisonTransitive relationPreferencePreference relationSimilarity (geometry)Mathematical economicsComputer scienceOrder (exchange)EconometricsMathematicsArtificial intelligenceEconomicsStatisticsCombinatorics

Abstract

fetched live from OpenAlex

Guided by evidence from eye‐tracking studies of choice, pairwise comparison is assumed to be the building block of the decision‐making procedure. A decision‐maker with a rational preference may nevertheless consider the constituent pairwise comparisons gradually, easier comparisons preceding difficult ones. Facing a choice problem, she may be unable to complete all relevant comparisons and choose with equal odds from alternatives not found inferior. Stochastic choice data consistent with such behavior are characterized and used to infer the underlying preference relation and the order of pairwise comparisons. The choice procedure offers a novel rationale for behavioral phenomena such as the similarity effect and violations of stochastic transitivity and regularity.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.330
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.170
GPT teacher head0.391
Teacher spread0.221 · 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