Bibliographic record
Abstract
This paper characterizes the efficient sequential equilibrium when a government uses indirect control to exert its authority.We develop a dynamic principal-agent model in which a principal (a government) delegates the prevention of a disturbance-such as riots, protests, terrorism, crime, or tax evasion-to an agent who has an advantage in accomplishing this task.Our setting is a standard dynamic principalagent model with two additional features.First, the principal is allowed to exert direct control by intervening with an endogenously determined intensity of force which is costly to both players.Second, the principal suffers from limited commitment.Using recursive methods, we derive a fully analytical characterization of the likelihood, intensity, and duration of intervention.The first main insight from our model is that repeated and costly interventions are a feature of the efficient equilibrium.This is because they serve as a punishment to induce the agent into desired behavior.The second main insight is a detailed analysis of a fundamental tradeoff between the intensity and duration of intervention which is driven by the principal's inability to commit.Finally, we derive sharp predictions regarding the impact of various factors on likelihood, intensity, and duration of intervention.We discuss these results in the context of some historical episodes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 source (direct Gemma or distilled Codex), 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".