System Engineering Heuristics for Complex Systems
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
Abstract Complex systems are challenging for engineers. In considering the challenges in addressing complex problems as well as designing and developing complex systems, the INCOSE Complex Systems Working Group (CSWG) Heuristics Focus Team, in conjunction with the INCOSE Heuristics Team, has considered a range of systems engineering heuristics that guide the engineering of complex systems. These heuristics provide some initial insight for understanding the engineering of complex systems. This work aims to identify, develop, analyze and curate these heuristics and their potential use in dealing with complexity and developing complex systems. This paper concludes that a range of beneficial heuristics have been identified that cover the breadth of complex problems, as assessed from multiple perspectives. This initial or preliminary set of heuristics needs to be tested through practice and use across the INCOSE community before effort is expended to make them more memorable, either individually, or as a set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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