The Role of Heuristics in Information Security Decision Making
Bibliographic record
Abstract
Inadvertent human errors (e.g., clicking on phishing emails or falling for a spoofed website) have been the primary cause of security breaches in recent years. To understand the root cause of these errors and examine practical solutions for users to overcome them, we applied the theory of bounded rationality and explored the role of heuristics (i.e., short mental processes) in security decision making. Interviews with 27 participants revealed that users rely on various heuristics to simplify their decision making in the information security context. Specifically, users rely on experts’ comments (i.e., expertise heuristic), information at hand, such as recent events (i.e., availability heuristic), and security-representative visual cues (i.e., representativeness heuristic). Findings also showed the use of other heuristics, including affect, brand, and anchoring, to a lesser degree. The results have practical and theoretical significance. In particular, they extend the literature by integrating bounded rationality concepts and elaborating “how” users simplify their security decision making by relying on cognitive heuristics.
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 | 0.017 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".