Voting on Multiple Issues: What to Put on the Ballot?
Bibliographic record
Abstract
We study a multi-dimensional collective decision under incomplete information. \nAgents have Euclidean preferences and vote by simple majority on each \nissue (dimension), yielding the coordinate-wise median. Judicious rotations of \nthe orthogonal axes ñthe issues that are voted upon ñlead to welfare improvements. \nIf the agentsí types are drawn from a distribution with independent \nmarginals then, under weak conditions, voting on the original issues is not optimal. \nIf the marginals are identical (but not necessarily independent), then \nvoting Örst on the total sum and next on the di§erences is often welfare superior \nto voting on the original issues. We also provide various lower bounds on \nincentive e¢ ciency: in particular, if agentsítypes are drawn from a log-concave \ndensity with I.I.D. marginals, a second-best voting mechanism attains at least \n88% of the Örst-best e¢ ciency. Finally, we generalize our method and some \nof our insights to preferences derived from distance functions based on inner \nproducts.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.011 |
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".