Integrating Defeaters into Subjective Logic-Based Quantitative Assurance Arguments
Bibliographic record
Abstract
A safety assurance argument is a structured reasoning process used to demonstrate that a system meets certain desired safety properties. The argument typically includes claims about the system, evidence supporting those claims, and a clear, logical connection between the evidence and the claims. A critical step in this process is the evaluation of confidence in the argument. To address this step, a range of qualitative and quantitative methods have been proposed. In the qualitative case, defeaters have been used as a dialectical means to challenge nodes in an argument. The presence of defeaters in an assurance argument may highlight reasoning or knowledge gaps, significantly undermining confidence in the argument's validity. However, it is not clear how defeaters can be incorporated into quantitative methods. In this paper, we formalize the notion of defeaters and demonstrate how Subjective Logic can be used to propagate belief, disbelief, and uncertainty within a quantitative assurance argument when these defeaters are present. As a result, this approach enhances the reliability of the argument, allowing for a more rigorous evaluation of safety in complex systems.
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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 | 0.047 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".