Choosing an Electoral Rule: Values and Self-Interest in the Lab
Bibliographic record
Abstract
Citizens are increasingly involved in the design of democratic institutions, for instance via referendums. If they support the institution that best serves their self-interest, the outcome inevitably advantages the largest group and disadvantages minorities. In this paper, we challenge this pessimistic view with an original lab experiment in France and Great Britain. In the first phase, experimental subjects experience elections under plurality and approval voting. In the second phase, they decide which rule they want to use for extra elections. The treatment is whether they do or do not have information to determine where their self-interest lies before deciding. We find that self-interest shapes people’s decisions, but so do intrinsic egalitarian values that subjects have outside of the lab. The implications are: (1) people have consistent ‘value-driven preferences’ for electoral rules, and (2) putting them in a situation of uncertainty leads to an outcome that reflects these values.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".