DEVS Modelling and Simulation of a Multi-Paradigm Modelling Tool
Bibliographic record
Abstract
Multi-Paradigm Modelling (MPM) has been proposed to tackle the complexities of the systems we build today. MPM proposes to explicitly model all relevant aspects of the system, at the right level(s) of abstraction, using the most appropriate formalism(s), while explicitly modelling the process. This imposes stringent requirements on any tool supporting MPM, such as support for distributed execution, support for multiple users, all while having acceptable performance. In this paper, we wish to aid the development of such complex tools by explicitly modelling the tool using the Parallel DEVS formalism, thereby applying the MPM approach ourselves. This model is applied in two domains: performance evaluation and model debugging. For performance, the DEVS model is used for what-if analysis and automated benchmarks. For debugging, DEVS debuggers are used instead of code debuggers, raising the level of abstraction. In both cases, DEVS yields deterministic execution, aiding in reproducibility.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".