License to Cheat: Voluntary Regulation and Ethical Behavior
Bibliographic record
Abstract
Although monitoring and regulation can be used to combat socially costly unethical conduct, their intended targets can often avoid regulation or hide their behavior. This surrenders at least part of the effectiveness of regulatory policies to firms' and individuals' decisions to voluntarily submit to regulation. We study individuals' decisions to avoid monitoring or regulation and thus enhance their ability to engage in unethical conduct. We conduct a laboratory experiment in which participants engage in a competitive task and can decide between having the opportunity to misreport their performance or having their performance verified by an external monitor. To study the effect of social factors on the willingness to be subject to monitoring, we vary whether participants make this decision simultaneously with others or sequentially, as well as whether the decision is private or public. Our results show that the opportunity to avoid being submitted to regulation produces more unethical conduct than situations in which regulation is either exogenously imposed or entirely absent. This paper was accepted by Uri Gneezy, behavioral economics.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".