Incentives for care, litigation, and tort reform under self-serving bias
Bibliographic record
Abstract
This chapter presents a strategic model of incentives for care and litigation under asymmetric information and self-serving bias, and studies the effects of damage caps. Our main findings are as follows. First, our results suggest that the defendant's bias decreases his expenditures on accident prevention, and hence, increases the likelihood of accidents. Second, both litigants' biases increase the likelihood of disputes. Third, our results indicate that, although self-serving bias help litigants commit on tough negotiation positions, it is economically self-defeating for the informed plaintiff. Fourth, our findings suggest that that the plaintiff's bias is always welfare reducing. The defendant's bias is welfare reducing if under-deterrence is present. We then illustrate the benefits of incorporating self-serving bias into the theoretical analysis of tort reform by studying the effects of damage caps. We find that this tort reform decreases the defendant's level of care if the biased defendant perceives the cap as relatively low. Importantly, we find that the positive effect of damage caps on lowering the likelihood of disputes, commonly attributed to this tort reform, might not necessarily be observed in environments with biased litigants: Caps might induce higher likelihood of disputes if the defendant perceives the cap as relatively low, and the plaintiff perceives the cap as relatively high. As a result, this tort reform might be welfare reducing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".