Entropy-centric framework for understanding and managing project dynamics in construction
Bibliographic record
Abstract
Traditional construction management methodologies often fail to address unforeseen challenges and uncertainties. This paper highlights that projects can exist in different states, often unidentified by project managers. These varying states necessitate different approaches, indicating that one-size-fits-all methods are insufficient. Using project data, entropy calculations, and simulations within a Design Science Research methodology, this paper offers indicators for evaluating project states and improving decision-making. The application of ChaosCompass to eight real-world projects showed higher entropy in projects exceeding budgets and schedules, indicating greater disorder and unpredictability. Conversely, projects on budget and schedule displayed more controlled progress. The findings reveal a significant correlation between high entropy and low forecast accuracy, underscoring entropy's critical role in project dynamics. This paper advocates an entropy-based approach to construction management, promising a more resilient and adaptable framework to address modern project complexities. • Introduced an entropy-based framework for better decision-making in construction management. • Enhanced interpretation of project dynamics using entropy analysis. • Integrated entropy to support proactive decision-making. • Demonstrated ChaosCompass's effectiveness in detecting high entropy in real-world projects. • Provided case studies showing the link between entropy and project predictability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".