Bayesian inference and model comparison for random choice structures
Bibliographic record
Abstract
Abstract. We consider an environment in which agents face various choice sets, assembled from a finite universe of objects, and choose a single object each time a choice set is presented to them. Models for probabilistic discrete choice give, for each choice set, a discrete probability distribution over that choice set. We use methods of Bayesian model comparison to measure the empirical plausibility of various axioms of probabilistic discrete choice. Our testing ground is a model with very little structure — a priori, there are no restrictions on choice distributions across choice sets. We reanalyze several existing data sets, including ones obtained using experimental designs intended to elicit intransitive revealed preferences. We find empirical evidence in favour of random utility, the hypothesis that all choice probabilities are governed by a random utility function over the universe of objects. We also find evidence against the multiplicative inequality of Sattath and Tversky (1976). Since the multiplicative inequality is a necessary condition for independent random utility, a refinement of random utility stipulating that the utilities of objects are mutually independent, this constitutes evidence against independent random utility. 1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".