Coupling concepts for simulation: A systematic and comprehensive view and advantages with declarative models
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
A brief review of the importance of simulation-based engineering and science (including social sciences) is followed by a historic perspective of model-based simulation. Section 2 is on declarative modeling of component systems as well as its advantages for self-documentation and for computer-aided checks and coupling. As an example for declarative modeling, General System Theory (GEST) implementor is given. In Sec. 3, basic concepts for coupling of component models, and rules for computer-assisted coupling specification are explained. Section 4 is devoted to possible computerized checks in couplings of declarative models such as: (1) automatic unit checking to avoid meaningless input/output matching at the time of coupling specification, (2) automatic threshold checking to provide warnings and/or to avoid disasters, and (3) automatic unit conversion for convenience of using library models. Section 5 is about several layers of nested couplings for modeling systems of systems. In Sec. 6, two types of variable couplings are discussed: (1) couplings with variable connections (to allow input/output relations of models to depend on time or state conditions) and (2) coupling with variable component models (to allow component (or coupled) models to be switched based on time or state conditions). Section 7 is on the use of multimodels as component models in couplings. Section 8 is on types of inputs and their use in couplings as well as on external inputs to simulation studies. In Sec. 9, conclusions and future work for complex systems are outlined. Especially, the values of simulation systems engineering as well as understanding and avoidance of misunderstanding in cognitive and emotive simulations are stressed. Appendix A is a list of almost 50 types of couplings and Appendix B lists over 50 terms related with couplings in modeling and simulation. To show the richness of "input" concept which is important in specification of input/output relations of component models, Appendix C lists almost 150 types of inputs. Information shared in this article may be useful in developing advanced modeling and simulation software, tools and environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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