Bayesian inference and model comparison for random choice structures
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it