How decoy options ferment choice biases in real-world consumer decision-making
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
The decoy effect describes a bias in which people's choices between two valuable options are swayed by a third, inferior, "decoy" option. Despite being documented in lab settings, relatively little work has investigated whether decoy effects occur "in the wild" where consumers face large, diverse choice sets. We employ a new methodology to examine the impact of decoy options on purchase decisions using a dataset of 3.6 million UK grocery-store wine transactions. Results indicate that when comparing wines that vary in quality and price across contexts, the presence of dominated (i.e., inferior) decoy options increased consumers' likelihood of choosing a target option-a hallmark of the well-documented attraction effect. The strength of these effects was modest overall (roughly 1% change in preference) and, interestingly, depended on consumers' idiosyncratic histories of experience. Our study provides a proof of principle demonstrating that these sorts of context effects are detectable in richer, complex real-world consumer choice settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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