Insurance purchasing under ambiguity, and its applications for forest carbon offsets: an experimental study
Bibliographic record
Abstract
The limitations of the expected utility theory in predicting risk preference under low probabilities have been discussed by various experimental studies. However, the ex-isting studies have not arrived at a consensus in this area. There are signs of both over-insurance and under-insurance for low-probability loss events. The topic has particularly not been addressed when ambiguity is coupled with low probability estimates. This paper theoretically analyzes the implications of ambiguity aversion for insurance purchasing in loss events involving low probabilities. The topic has been looked into under the light of forest carbon offsets and the need for insurance for unavoidable losses of the sequestrated carbon. The paper offers an experimental design involving three phases, including a replica of a previous study on insurance behaviour, addition of the ambiguity factor, and two methods of measuring ambiguity preference. Wildfire losses are associated with small probabilities and ambiguity, and ambiguity increases the individual's willingness to pay for insurance. Therefore, the government can set a higher price for its mandatory insurance program provided on forestry offset credits.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".