The Theory of Weak Revealed Preference
Bibliographic record
Abstract
Given a finite collection of choices over budgets, rational consumer behavior is identified with the generalized axiom of revealed preference (GARP). As an alternative, the weak generalized axiom of revealed preference--WGARP--only rules out strict choice cycles of size two. While GARP failures are routinely found in both the field and the lab, the same is not true for WGARP. We study how WGARP-consistent choices can be rationalized as a form of optimal behavior. Our main result is an analogue of the celebrated Afriat's theorem, but for WGARP. We show that choices are consistent with WGARP if and only if they can be rationalized by an asymmetric and strictly monotonic preference function. Equivalently, they can be rationalized by a preference function that admits a coalitional multi-utility (CMU) representation with a coherence restriction. A coherent CMU representation aggregates multiple utility functions --selves-- within the individual, so that binary preference reversals do not occur, while longer preference cycles remain possible. As a result, the class of incomplete, intransitive, and nonconvex preferences compatible with WGARP is studied.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".