Application of Computational Simulation in Organizational Research
Bibliographic record
Abstract
This presenter symposium brings together researchers from diverse fields of organizational research to present their work using computational simulation. The first paper conducted by Cronin and Vancouver illustrates how to make theoretical models that have a dynamic component and how simulation helps us not only to test such models, but also to think through them in the first place. The next paper conducted by Will explains how the relationship between collective outcomes and the individual-to-individual interaction patterns from which they emerge can first be fleshed out by phenomena-based modeling and then further refined by exploratory modeling in Netlogo's flocking model. The third study conducted by Trinh and Bao continues to explore the application of computational simulation to investigate the group phenomenon. In the last study, Kennedy, Sommer, and Nguyen applied Virtual experimentation to investigate organizational members' behaviors and interactions on large-scale projects using multi-team systems (MTS). Our purpose for this symposium is to clarify what simulations are and how they work. We attempt to provide a roadmap for how to apply computational simulation methods to our own research and encourage the management theorists to appreciate and best garner the benefits of simulation methods.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".