Learning and Coordination in the Presidential Primary System
Bibliographic record
Abstract
In elections with three or more candidates, coordination among like-minded voters is an important problem. We analyse the trade-off between coordination and learning about candidate quality under different temporal election systems in the context of the U.S. presidential primary system. In our model, candidates with different policy positions and qualities compete for the nomination, and voters are uncertain about the candidates' valence. This setup generates two effects: vote splitting ( i.e . several candidates in the same policy position compete for the same voter pool) and voter learning (as the results in earlier elections help voters to update their beliefs on candidate quality). Sequential voting minimizes vote splitting in late districts, but voters may coordinate on a low-quality candidate. Using the parameter estimates obtained from all the Democratic and Republican presidential primaries during 2000–12, we conduct policy experiments such as replacing the current system with a simultaneous system, adopting the reform proposal of the National Association of Secretaries of State, or imposing party rules that lead to candidate withdrawal when prespecified conditions are met.
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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 | 0.003 | 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".