Bibliographic record
Abstract
Two candidates compete in an election. Each candidate chooses a position in a given set, and prefers to win than to tie than to lose. Each member of a finite set of citizens has preferences over positions, and votes for the candidate whose position she prefers. The candidates know the citizens' preferences. In any Nash equilibrium of the strategic game in which the candidates are the players, each candidate's position is a Condorcet winner of the collective choice problem in which the individuals are the citizens. So if the citizens' preferences are single-peaked, each candidate's position in a Nash equilibrium is the median of the individuals' favorite positions, and if the preferences are single-crossing, it is the favorite position of the median citizen. If the candidates choose their positions sequentially, these positions are also Condorcet winners if such winners exist. Suppose that the candidates are uncertain of the citizens' preferences and the set of positions is an interval of real numbers. If the candidates share the belief that the median of the citizens' favorite positions has a given distribution, in an equilibrium they both choose the median of that distribution. If the candidates are privately informed about the median, the equilibrium position of a candidate with a given signal is the median of the distribution when the other candidate's signal is the same. The chapter explores also a model in which voting is costly and one in which citizens have preferences over candidates independent of the positions chosen by the candidates.
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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 | 0.001 | 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".