13. Two-period electoral competition with imperfect information
Bibliographic record
Abstract
Suppose that an incumbent of unknown quality chooses a policy, and a citizen with limited information about the incumbent observes the outcome and either reelects the incumbent or elects a challenger. If the citizen does not know the incumbent's preferences, and the incumbent's payoff to holding office is high, then there is an equilibrium in which all incumbents choose a policy that the citizen likes. If all incumbents' preferences conflict with the citizen's, effort is required by the incumbent to produce an outcome the citizen likes, and the incumbent's payoff to holding office is high, then there are equilibria in which the incumbent exerts low effort and also ones in which she exerts high effort and is reelected only if the outcome is good for the citizen. If the citizen is uncertain about the policy that is best for her, which depends on a state the incumbent knows, and the incumbent's payoff to holding office is high, then some incumbents choose a policy the citizen believes is best for her, even if the incumbents know that it is not. In this case, the policy chosen by a random citizen may be better for the citizen.
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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 | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".