Can you really not see this black swan coming? Managing climate risks in an insurance company
Bibliographic record
Abstract
Insurance companies still struggle to quantify the impacts of climate-related risks (CRs), which contributes to the underestimation of these risks despite worsening climate change. Most research on this topic focuses on technical improvements to risk management, often neglecting psychological factors. This paper explores how the dynamics between psychological distance, abstraction, and quantification influence CR management within an insurance company. In total, 28 semi-structured interviews and one and a half years of participant observation were conducted in a large Canadian insurance company. Drawing on construal level theory and the literature on the sociology of quantification, the findings reveal that psychologically distant and abstract mental representations of CRs, coupled with uncertainty in quantifying their impacts in a number-driven industry, reduce people's perceptions of the probability and importance of CRs. This study contributes to the literature by offering novel theoretical perspectives on how psychological factors influence the management of CRs. It also suggests managerial implications, such as the need to integrate qualitative tools alongside quantitative models to improve the management of complex risks. Lastly, future research avenues are discussed.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".