Incentivized Torts: An Empirical Analysis
Bibliographic record
Abstract
Courts and scholars assume that group causation theories deter wrongdoers. This Article empirically tests, and rejects, this assumption, using a series of incentivized laboratory experiments. Contrary to common belief and theory, data from over 200 subjects show that group liability can encourage tortious behavior and incentivize individuals to act with as many tortfeasors as possible. We find that subjects can be just as likely to commit a tort under a liability regime as they would be when facing no tort liability. Group liability can also incentivize a tort by making subjects perceive it as fairer to victims and society. These findings are consistent across a series of robustness checks, including both regression analyses and nonparametric tests. We also test courts’ and scholars’ insistence that the but-for test fails in cases subject to group causation. We use a novel experimental design that allows us to test whether, and to what extent, each individual’s decision to engage in a tortious activity is influenced by the decisions of others. Upending conventional belief, we find strong evidence that the but-for test operates in group causation settings (e.g., concurrent causes). Moreover, across our experiments, subjects’ reliance on but-for causation produced the very tort that group liability attempted to discourage. A major function of liability in torts, criminal law, and other areas of the law is to deter actors from engaging in socially undesirable activities. The same is said about doctrines that result in group liability. Our empirical results challenge this basic logic
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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 | 0.020 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".