The Normalization of Consumer Valuations: Context-Dependent Preferences from Neurobiological Constraints
Why this work is in the frame
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Bibliographic record
Abstract
Consumer valuations are shaped by choice sets, exemplified by patterns of substitution between alternatives as choice sets are varied. Building on recent neuroeconomic evidence that valuations are transformed during the choice process, we incorporate the canonical divisive normalization computation into a discrete choice model and characterize how choice behaviour depends on both size and composition of the choice set. We then examine evidence for such behaviour from two choice experiments that vary the size and composition of the choice set. We find that divisive normalization more accurately captures observed behaviour than alternative models, including an example range normalization model. These results are robust across experimental paradigms. Finally, we demonstrate that Divisive Normalization implements an efficient means for the brain to represent valuations given neurobiological constraints, yielding the fewest choice errors possible given those constraints. This paper was accepted by Elke Weber, judgment and decision making.
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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 | 0.000 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| 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