Fuzzy Agent–Based Modeling of Competency and Performance Measures in Construction
Bibliographic record
Abstract
Construction organizations are project-based organizations in which both competencies and performance develop through project execution. A competency measure is a combination of knowledge, skill, processes, practices, and/or technology possessed by a construction organization. Performance measures are used to assess an organization’s competitiveness within its industry. An organization can analyze competencies to predict and improve performance. Previous studies investigated project- and organization-level competencies with respect to performance measures. However, multilevel assessment is needed, although it is challenging because the nature of construction competency and performance measures is complex, dynamic, and subjectively uncertain. To address these challenges, this paper presents a methodology for developing a fuzzy agent–based model (FABM) of competency and performance that models a set of organizational- and project-level competency measures as decision-making entities (i.e., agents) and predicts multiple performance measures as their emergent behavior. A case study was implemented to demonstrate, verify, and validate the proposed FABM model, with encouraging results in performance prediction. The academic contribution of this paper is providing a novel systematic, bottom-up modeling approach for measuring and assessing competencies at the project and organization levels and mapping these multilevel competencies to construction performance measures. Furthermore, the outcomes of this study are expected to support construction practitioners by providing a set of comprehensive hierarchical competency and performance metrics at the project and organization levels for managerial actions taken to identify, construct, and develop competency models to assess performance at both levels.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".