REINSURANCE ANALYTICS USING SERIAL AND PARALLEL COMPUTATION ON THE MULTIOBJECTIVE EVOLUTIONARY ALGORITHM SPEA2
Bibliographic record
Abstract
This paper presents a novel and efficient application of the SPEA2 on reinsurance contract optimization considering the perspective from an insurance company. The reinsurance operation aims to transfer the risk taken by an insurance company, usually against natural catastrophes, to a bigger corporation. The process of reinsurance is similar to that one where a client wants to insure his properties upon the payment of a premium. Then, the insurance company sells his portfolio using the reinsurance market aiming to maximize the expected return and, at the same time, to maximize the risk hedged to the reinsurance company. This problem is naturally multi-objective, consequently the SPEA2 algorithm appears as an attractive approach to tackle the problem. Results show that the SPEA2 can obtain better outcomes than a sophisticated algorithm called enhanced MO-PBIL in terms of hypervolume. A parallel version based on the master-slave model showed that a speedup of 1.83 can be reached using 4 cores and 500 iterations, and 2.13 using 8 cores and 100 iterations, with little effort to parallelize the application.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".