Risk‐layering and optimal insurance uptake under ambiguity: With an application to farmers exposed to drought risk in Austria
Bibliographic record
Abstract
Many risks we face today will very likely not stay the same over time. For example, it is expected that climate change will alter future risks of natural disaster events considerably and, as a consequence, current risk management and governance strategies may not be effective anymore. Large ambiguities arise if future climate change impacts should be taken into account for analyzing risk management options today. Risk insurance, while albeit only one of many risk management actions possible, plays an important role in current societies for dealing with extremes. A natural starting point for our analysis is therefore the question of how ambiguity may be incorporated in a world with changing risks. To shed light on this question, we study how ambiguity can affect the uptake of insurance and risk mitigation within a risk-layer approach where each layer is quantified using distortion risk measures that should reflect the risk aversion of a decisionmaker toward extreme losses. Importantly, we obtain a closed-form solution for such a problem statement which allows an efficient numerical implementation. We apply this model to a case study of drought risk for Austrian farmers and address the question how ambiguity will affect the risk layers of different types of farmers and how subsidies may help to deal with current and future risks. We found that especially for small-scale farmers the consequences of increasing risk and model ambiguity are pronounced and subsidies are especially needed in this case to cover the high-risk layer.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".