Leaving the store empty‐handed: Testing explanations for the too‐much‐choice effect using decision field theory
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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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 | 0.010 | 0.048 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".