Fast-and-frugal heuristics: an exploration into building an adaptive toolbox to assess the uncertainty of rework
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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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".