When Smaller Menus Are Better: Variability in Menu-Setting Ability
Bibliographic record
Abstract
Are large menus better than small menus? Recent literature argues that individuals' apparent preference for smaller menus can be explained by their behavioral biases or informational limitations. These explanations imply that absent behavioral or informational effects, larger menus would be objectively better. However, in an important economic context—401(k) pension plans—we find that larger menus are objectively worse than smaller menus, as measured by the maximum Sharpe ratio achievable. We propose a model in which menu setters differ in their ability to preselect the menu. We show that when the cost of increasing the menu size is sufficiently small, a lower-ability menu setter optimally offers more items in the menu than a higher-ability menu setter. Nevertheless, the menu optimally offered by a higher-ability menu setter remains superior. This results in a negative relation between menu size and menu quality: smaller menus are better than larger menus. This paper was accepted by Wei Xiong, finance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".