The Role of Smart Heuristics in Decision-Making Under Uncertainty: Migitating Rework and Its Consequences
Bibliographic record
Abstract
Decision-making under risk in construction is typically aligned with the classical rationalist view. Here, statistical methods and formal logic are applied to select the best possible solution from a series of alternatives drawn from credible information to determine their risks. However, in the case of rework—one of the most expensive problems facing construction organizations—its risks cannot be determined due to the absence of information about its determinants. Rework is characterized by uncertainty, and thus, the use of mathematical prescriptions to predict its occurrence is highly questionable. In contrast to the classical view of rationality, we suggest in this article that smart heuristics framed within the bounds of ecological rationality provide an effective decision strategy to accommodate the uncertainty of rework in construction. Using an illustrative case study approach, we demonstrate how fast-and-frugal trees (FFT), a smart heuristic, can be retrospectively constructed from real-world rework events where major safety incidents also occurred and were used as a reference source for decision-making. The research and practical implications of adopting the conceptual lens of smart heuristics to accommodate rework in construction are then discussed.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".