How decoy options ferment choice biases in real-world consumer decision-making
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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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".