Reviewing linkages between display design and cognitive biases in decision making: an emergency response perspective
Bibliographic record
Abstract
Decision making in nuclear emergency response involves significant uncertainty, time pressure, and high stakes. The stress and ambiguity can result in the use of heuristic decision making strategies, which may sometimes result in poor decision outcomes. As complex information systems are often used in emergency operations, this research aims at examining linkages between design choices in computer displays and cognitive biases. This paper reports a literature review covering a set of themes that include framing effects, facilitating balanced perspectives of information, automation transparency, and the role of meta-information in conveying significance. The review provides evidence from the literature showing the importance of display design in mitigating the emergence of cognitive biases in decision making tasks. The review resulted in 11 guidelines for the design of computer displays in safety-critical applications, and directions are provided for future research. A theme that connects many of these guidelines is that providing the big picture can mitigate many types of biases, which often should encompass self-reflection and calibration of appropriate confidence levels. It can be concluded that over time, the potential for biasing users can be interwoven within computer displays if debiasing is not taken into consideration early in the design 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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".