Can Independent Underwriters BenefitInsurers in High-Risk Lines?A Cournot Market-Game Analysis
Why this work is in the frame
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Bibliographic record
Abstract
One of the greatest dangers to the solvency of property-liability insurers is writing large amounts of new business in a high-risk line (i.e., a line of insurance in which a substantial portion of buyers consists of high-risk insureds). This practice is problematic because of both potentially inadequate pricing and potentially lax underwriting. A prominent example of the latter phenomenon was the collapse of many captive insurers in the early to mid-1980s, in which the insurers relied too heavily on independent underwriters motivated solely by increasing premium volume. In this article, we employ a Cournot market-game model to study the financial impact of informed independent underwriters (i.e., unaffiliated underwriters with private information regarding the risk characteristics of insureds) on insurers in high-risk property-liability lines. In a market with a risk-neutral insurer and CARA insureds, we find that the insurer will always do worse by using a risk-neutral underwriter than by operating on a direct-writing basis. However, for an insurer employing mean-variance optimization, the proper combination of underwriter-compensation and capital allocation may lead to better outcomes than direct writing.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it