Can Independent Underwriters BenefitInsurers in High-Risk Lines?A Cournot Market-Game Analysis
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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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".