Integrating Abstract State Machines and Interpreted Systems for Situation Analysis decision support design
Bibliographic record
Abstract
Abstract—A formal approach to the design of situation analysis and decision support systems is justified and unavoidable if one is interested in reproducibility/traceability of results, satisfaction of constraints, and a language to represent and reason about dy-namic situations. In this paper, we propose the integration of two multiagent modeling paradigms, Abstract State Machines and Interpreted Systems, to develop a comprehensive framework for computational Situation Analysis (SA) as a basis for design and development of decision support systems. Due to the similarities of the underlying modeling concepts, a systematic integration of the two paradigms seems sensible, as each one has its particular focus and strength, complementing each other in several respects. Our approach builds on multiagent systems theories to formalize the distributed aspect, allows for reasoning about knowledge, uncertainty and belief change, and enables rapid prototyping of abstract executable decision support system models.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".