Revealed preference axioms for endogenous consideration set formation
Why this work is in the frame
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Bibliographic record
Abstract
We consider a setting in which the consideration sets being formed by a decision maker are observable. We analyze the necessary and sufficient conditions under which the observed sets are consistent with endogenous consideration set formation. In particular, we rationalize the consideration sets as being optimally formed by a decision maker who faces costly attention and is forced to choose a subset of alternatives to pay attention to. We show that axioms similar to those from revealed preference theory allow us to do this. The most general model is characterized by a condition resembling the Strong Axiom applied on a domain of sets rather than individual alternatives. Since the idea of observable consideration sets seems realistic in a random choice framework in which we can interpret zero probability of being chosen as the alternative being omitted from the consideration set, we apply our result to this setting using the Logit model. This results in a representation theorem for a generalized version of the Logit model.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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