A DSM-based multi-paradigm simulation modeling approach for complex systems
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
Complex systems contain hierarchical heterogeneous subsystems and diverse domain behavior patterns, which bring a grand challenge for simulation modeling. To cope with this challenge, the M&S community extends their existing modeling paradigms to promote reusability, interoperability and composability of simulation models and systems; however, these efforts are relatively isolated and limited to their own technical space. In this paper, we propose a domain specific modeling (DSM)-based multi-paradigm modeling approach which utilizes model driven engineering techniques to integrate current M&S paradigms and promote formal and automated model development. This approach constructs a simulation model framework to architect the structure of the overall simulation system and combines multiple M&S formalisms to describe the diverse domain behaviors; moreover, it provides domain specific language and environment support for conceptual modeling based on the model framework and formalisms. An application example on combat system effectiveness simulation illustrates the applicability of the approach.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it