Risk-informed decision models for low-probability, high-consequence hazards
Bibliographic record
Abstract
Risk mitigation decisions for civil infrastructure exposed to rare natural and manmade hazards are often impacted by risk aversion, a behavioral phenomenon in which the decision maker's perception and judgment of risk are systematically distorted, resulting in decisions that might be viewed as excessively conservative when compared to those from a traditional minimum expected cost analysis. This study addresses how decisions regarding structural safety are affected by the attitudes of the decision-maker toward risk using decision models, such as cumulative prospect theory, that allow risk-averse behaviors to be modeled quantitatively. Perspectives on the general characteristics of risk-aversion are first drawn from risk pricing techniques in the insurance industry. These perspectives are then refined for structural engineering applications by investigations of decisions involving seismic retrofit of unreinforced masonry structures in San Francisco, CA and aseismic design of a steel moment frame in Vancouver, BC. Risk attitudes when confronting extreme wind hazards are also assessed using a decision by the North and South Carolina Code Councils to waive a provision in the International Residential Code that would have required additional windborne debris protection in residential construction. An examination of risk attitudes toward competing natural hazards is then introduced by comparing decisions related to wind and seismic effects in areas where both hazards may be significant. These investigations have led to tentative conclusions regarding the role of risk aversion in the assurance of structural safety and in code-related decisions and suggest avenues for future study.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".