Who Should We Fear More: Biohackers, Disgruntled Postdocs, or Bad Governments? A Simple Risk Chain Model of Biorisk
Bibliographic record
Abstract
The biological risk landscape continues to evolve as developments in synthetic biology and biotechnology offer increasingly powerful tools to a widening pool of actors, including those who may consider carrying out a deliberate biological attack. However, it remains unclear whether it is the relatively large numbers of low-resourced actors or the small handful of high-powered actors who pose a greater biosecurity risk. To answer this question, this paper introduces a simple risk chain model of biorisk, from actor intent to a biological event, where the actor can successfully pass through each of N steps. Assuming that actor success probability at each independent step is sigmoidally distributed and actor power follows a power-law distribution, if a biorisk event were to occur, this model shows that the expected perpetrator would likely be highly powered, despite lower-powered actors being far more numerous. However, as the number of necessary steps leading to a biological release scenario decreases, lower-powered actors can quickly overtake more powerful actors as the likely source of a given event. If steps in the risk chain are of unequal difficulty, this model shows that actors are primarily limited by the most difficult step. These results have implications for biosecurity risk assessment and health security strengthening initiatives and highlight the need to consider actor power and ensure that the steps leading to a biorisk event are sufficiently difficult and not easily bypassed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".