Extending Explicitly Modelled Simulation Debugging Environments with Dynamic Structure
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
The widespread adoption of Modelling and Simulation (M8S) techniques hinges on the availability of tools supporting each phase in the M8S-based workflow. This includes tasks such as specifying, implementing, experimenting with, as well as debugging, simulation models. We have previously developed a technique where advanced debugging environments are generated from an explicit behavioural model of the user interface and the simulator. These models are extracted from the code of existing modelling environments and simulators and instrumented with debugging operations. This technique can be reused for a large family of modelling formalisms but was not yet considered for dynamic-structure formalisms; debugging models in these formalisms is challenging, as entities can appear and disappear during simulation. In this article, we adapt and apply our approach to accommodate dynamic-structure formalisms. To this end, we present a modular, reusable approach, which includes an architecture and a workflow. We observe that to effectively debug dynamic-structure models, domain-specific visualizations developed by the modeller should be (re)used for debugging tasks. To demonstrate our technique, we use Dynamic-Structure DEVS (a formalism that includes the characteristics of discrete-event and agent-based modelling paradigms) and an implementation of its simulation semantics in the PythonPDEVS tool as a running example. We apply our technique on NetLogo, a popular multi-agent simulation tool, to demonstrate the generality of our approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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