Evolutionary game dynamics of index insurance with refund or non-refund risk sharing mechanism
Bibliographic record
Abstract
Index insurance, by utilizing preset indices, effectively mitigates the moral hazards and adverse selection inherent in traditional insurance. Yet, the mismatch between these indices and actual losses, known as basis risk, severely hinders its broader adoption. The existing study has proposed a risk sharing mechanism, in which individuals who have not suffered losses but have received compensation assist those who have suffered losses but have not received compensation. Nonetheless, due to individual profit-seeking behavior, this mechanism is difficult to implement in practice. In this study, we construct a threshold-discounted index insurance game model including new risk sharing mechanisms. In the model, individuals purchasing index insurance are required to contribute to a risk sharing pool to compensate those facing basis risk. We analyze two mechanisms for handling the funds: a refund mechanism and a non-refund mechanism, depending on whether the funds are returned to the policyholders when no basis risk occurs. We find that low basis risk remains key for the sale of index insurance under both mechanisms. Moreover, with an appropriate risk sharing ratio, the introduction of a risk sharing pool significantly increases the adoption of index insurance. The refund mechanism, with its stronger risk sharing capability, proves more popular. Additionally, larger group sizes or lower collective thresholds can enhance the stability and aid capacity of the risk sharing pool, thereby further increasing adoption rates. Finally, insurance companies should accurately assess the risk aversion level of the population, as it is also a key factor affecting insurance sales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".