Fast-and-frugal heuristics: an exploration into building an adaptive toolbox to assess the uncertainty of rework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Performing rework within the production system of construction is the most expensive waste that confronts organisations, with its causation yet to be fully understood in practice.Any effort to assess the risk of rework poses challenges due to limited information about its frequency and causes, rendering the use of statistical models immeasurable.Research has shown that fast-and-frugal heuristics enable epistemic success under conditions of uncertainty and cognitive complexity -they are accurate, fast, and rely on limited information.Thus, this paper proposes the following research question: How can fast-and-frugal heuristics effectively assess the uncertainty of rework in construction?The theoretical framing of ecological rationality provides an environmental structure for bounded rationality to explore this question, enabling a person's 'adaptive toolbox' of fast-and-frugal heuristics tailored for different epistemic and pragmatic decisions to be utilised.Situations during the construction of a transport infrastructure mega-project (>AU$18 billion) where there was profound uncertainty surrounding rework are presented.The heuristics, intuitively drawn from an individual's adaptive toolbox used to form judgments to assess the uncertainty of rework, are identified.The theoretical and practical implications of the paper are discussed before presenting suggestions for future research to help build a robust adaptive toolbox to be utilised for assessing the uncertainty of rework in construction.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it