Projects as Dynamic, Multi-level temporary Organizations: Advantages of an Agent-Based Modeling Approach
Bibliographic record
Abstract
Projects are complex systems. They are dynamic, uncertain, heterogeneous entities embedded within social, organizational, and broader contexts. Agent-based modeling (ABM) is a computational method that allows for the modeling of autonomous, heterogeneous, and interacting agents in a multi-level system. The re-conceptualized view of projects discussed in the literature supports the notion of projects as dynamic, multi-level temporary organizations. Through this lens, we argue that an ABM approach provides key advantages for understanding and exploring relevant topics in project management. The features that make temporary organizations challenging to understand and explore, including temporality, behavioral considerations, and embeddedness, are also areas where ABM could prove advantageous. We also address the difficulties associated with using ABM in this context and do not claim that ABM is the only method for addressing these challenges. The goal of this paper is to provide a computational perspective from which to think about and further explore research in project management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| 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".