Burning biases: Mitigating cognitive biases in fire engineering
Bibliographic record
Abstract
Summary Fire engineering has developed into a mainstream engineering discipline within the building design process. Building fire codes are increasingly complex, comprising thousands of requirements regarding a wide range of topics that must be considered. Fire engineers are required to possess increasingly complex knowledge about a variety of subjects, along with expertise in their application. This has been magnified with the proliferation of performance‐based methods using a range of computational tools. This coupled with increased project performance pressures, raises the potential for errors in judgment. Errors in judgment may be caused by limitations in a given resource (e.g. time, information available, knowledge, etc) and/or neglect/over‐focus on specific information (at the expense of other and more relevant information) through cognitive biases. This paper initially provides a broad overview of general decision‐making, including the use of heuristics and cognitive biases. Examples of cognitive biases are presented which may be linked to errors in fire engineer decision‐making. This study considers several fire engineering decision contexts where cognitive biases may exist which are associated with fire code application, modeling/calculations, probabilistic risk assessments, general fire engineering practice, and perceptions based on experience. Potential measures to mitigate some of these biases and prompt better decision‐making are discussed. Those that may benefit from awareness of such biases and mitigation measures include not only practicing fire engineers, but also building developers, fire code committees, evacuation/fire/structural fire modeling developers, approving authorities, and fire engineering researchers/students.
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 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".