Leaving the store empty‐handed: Testing explanations for the too‐much‐choice effect using decision field theory
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Economic theories of choice suggest that more options are better, and people should prefer choosing from among more options to find their most valued alternative. But in an intriguing counter‐example, Iyengar and Lepper (2000) observed that while people were attracted to more options while shopping, the larger set size increased the likelihood that they would leave the store empty‐handed. Surprisingly, this too‐much‐choice effect has not been consistently observed in situations where it would be expected (e.g., Chernev, 2003; Scheibehenne, 2008). This paper describes boundary conditions for the too‐much‐choice effect that were determined by evaluating three different psychological explanations within a unified theoretical framework, decision field theory (Busemeyer & Townsend, 1993). The effect of environmental structure on choice was also tested by varying the distribution of quality in the option sets between low variance (roughly uniform) and high variance (exponential distribution). Based on these simulations, two explanations were identified that differentially predicted the too‐much‐choice effect: avoiding choice when the most preferred option changes too often, or when time runs out. Moreover, the magnitude of the too‐much‐choice effect depended on the distribution of option quality. These mechanism environment structure combinations can help explain why the too‐much‐choice effect is observed some—but not all—of the time. © 2009 Wiley Periodicals, Inc.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| 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