Ordered Leniency: An Experimental Study of Law Enforcement with Self-Reporting
Bibliographic record
Abstract
This paper reports the results of an experiment designed to assess the ability of an enforcement agency to detect and deter harmful short-term activities committed by groups of injurers.With ordered-leniency policies, early cooperators receive reduced sanctions.We replicate the strategic environment described by Landeo and Spier (2018).In theory, the optimal ordered-leniency policy depends on the refinement criterion applied in case of multiplicity of equilibria.Our findings are as follows.First, we provide empirical evidence of a "race-to-the-courthouse" effect of ordered leniency: Mild and Strong Leniency induce the injurers to self-report promptly.These findings suggest that the injurers' behaviors are aligned with the risk-dominance refinement.Second, Mild and Strong Leniency significantly increase the likelihood of detection of harmful activities.This fundamental finding is explained by the high self-reporting rates under orderedleniency policies.Third, as a result of the increase in the detection rates, the averages fines are significantly higher under Mild and Strong Leniency.As expected when the risk-dominance refinement is applied, Mild Leniency exhibits the highest average fine.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".