Introduction to parallel DEVS modelling and simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
DEVS is a popular formalism for modelling complex dynamic systems using a discrete-event abstraction. Main advantages of DEVS are its rigorous formal definition, and its support for modularity: models can be hierarchically nested. Thanks to these properties, DEVS frequently serves as a simulation assembly language to which models in other formalisms are mapped. This makes it possible to combine models in different formalisms together by mapping both to DEVS. This tutorial introduces the practical use of the Parallel DEVS formalism in a bottom-up fashion. We start from simple autonomous Atomic (i.e., non-hierarchical) DEVS models and increment up to Coupled (i.e., hierarchical) DEVS models. Each increment is illustrated with a minimal running example. The focus is on the practical use of DEVS modelling and simulation, though necessary theoretical foundations are interleaved. Examples are presented using Python-PDEVS, though the foundations and techniques apply to other DEVS simulation tools as well.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it