Comprehensive Risk Assessment and Underwriting Investment Decision-Making Based on the EWM-TOPSIS Method
Bibliographic record
Abstract
The escalating frequency of extreme weather events poses a critical challenge for both property owners and insurers. Not only is property insurance becoming more costly, but it is also increasingly difficult to obtain. This article discusses how the best construction of the property insurance system to solve the profit crisis of the insurance company, The first task is to evaluate the serious weather in the area. Through search for related documents, the article determines the five remarkable features that describe serious weather events (temperature, precipitation, carbon dioxide concentration, humidity, air pressure), and then establish a risk evaluation system. In response to these indicators, use the EWM-TOPSIS method to evaluate 20 regions. The results of the K-MEANS cluster analysis are used to divide these areas into three categories: high risk, medium risk and low risk. By calculating the mathematical expectation of the insurance profit, it is expected to determine whether the area is worth investing, and put forward insurance pricing strategies in different risk areas, and develop insurance pricing models in different risk areas. Finally, two regions that have undergone two major weather events on different continents (Toronto in Canada and Queensland in Australia) use models to prove the feasibility of investment. The article also proposes that insurance owners can take some measures to affect the decision -making of insurance. Overall, through this model, it can help determine the areas that are most suitable for investment.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".