Strategic competition in informal risk-sharing mechanisms versus collective index insurance
Bibliographic record
Abstract
The frequent occurrence of natural disasters has posed significant challenges to society, necessitating the urgent development of effective risk management strategies. From the early informal community-based risk-sharing mechanisms to modern formal index insurance products, risk management tools have continuously evolved. Although index insurance provides an effective risk transfer mechanism in theory, it still faces the problems of basis risk and pricing in practice. At the same time, in the presence of informal community risk-sharing mechanisms, the competitiveness of index insurance deserves further investigation. Here, we propose a three-strategy evolutionary game model, which simultaneously examines the competitive relationship between formal index insurance purchasing ([Formula: see text]), informal risk-sharing strategies ([Formula: see text]) and complete non-insurance ([Formula: see text]). Furthermore, we introduce a method for calculating insurance company profits to aid in the optimal pricing of index insurance products. We find that basis risk and risk loss ratio have significant impacts on insurance adoption rate. Under scenarios with low basis risk and high loss ratios, index insurance is more popular; meanwhile, when the loss ratio is moderate, an informal risk-sharing strategy is the preferred option. Conversely, when the loss ratio is low, individuals tend to forego any insurance. Furthermore, accurately assessing the degree of risk aversion and determining the appropriate ratio of risk sharing are crucial for predicting the future market sales of index insurance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".