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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".