Evolution of the discontinuity concept in modeling and simulation: from original idea to model switching, switchable understanding, and beyond
Bibliographic record
Abstract
The GEneral System Theory implementer (GEST) was the first system-theory-based simulation model specification language. Even though a commercial implementation of GEST was not realized, over the years interesting ideas emerged based on it. At the beginning of this sequence of developments was the realization of two types of discontinuity in the integration of piecewise continuous systems. Afterwards, concepts generalized from the discontinuity processing were applied: (a) to modeling as a model update, multimodels, model switching, DNA-based modeling, dynamic model coupling, and dynamic model composability; (b) to simulations as multisimulation; (c) to psychology as a personality update and dynamic personality in human behavior simulation; (d) to understanding as multiunderstanding, switchable understanding, and misunderstanding avoidance; and (e) to emotion understanding as multiunderstanding and switchable understanding for better emotional intelligence as they are applied to the emotions of self and others. Each one of these areas may be the basis for some advanced modeling and simulation methodologies. Even though references are given for them, the emphasis in this article is the applicability of the same two discontinuity concepts to many different areas.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".