Predicting Peril or the Peril of Prediction? Assessing the Risk of CBRN Terrorism
Bibliographic record
Abstract
Since the mid-1990s, academic and policy communities have debated the risk posed by terrorist use of chemical, biological, radiological, or nuclear (CBRN) weapons. Three major schools of thought in the debate have emerged: the optimists, the pessimists, and the pragmatists. Although these three schools of thought draw on the same limited universe of data on CBRN terrorism, they arrive at strikingly different conclusions. Given the highly subjective process of CBRN terrorism risk assessment, this article analyzes the influence of mental shortcuts (called heuristics) and the systemic errors they create (called biases) on the risk assessment process. This article identifies and provides illustrative examples of a range of heuristics and biases that lead to the underestimation of risks, the overestimation of risks and, most importantly, those that degrade the quality of the debate about the level of risk. While these types of biases are commonly seen as affecting the public's perception of risk, such biases can also be found in risk assessments by experts. The article concludes with recommendations for improving the CBRN risk assessment process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".