Systems Engineering Heuristics for Complex Systems Revisited
Bibliographic record
Abstract
Systems of Systems are inherently complex, and hence often, traditional Systems Engineering (SysE) approaches may be inadequate. To assist, the INCOSE Complex Systems Working Group seeks to create and develop a useful set of SysE heuristics that can provide guidance. Analysis of an initial set of heuristics, identified using complex(ity) search terms across an INCOSE database, indicated that they did not sufficiently cover the wider system of interest and culture aspects and required further independent review and usage to become established. This paper addresses these concerns by using additional search terms across the INCOSE database and reporting the findings of an independent SysE team review of the original and newly identified heuristics. Using this approach, an additional 15 heuristics have been added, and modifications to both sets have been identified and agree with an independent focus group. It is concluded that the heuristics identified are useful and have resolved the breadth issues. However, to ensure the heuristics are more useful, additional work is required to rationalize the set from 33 heuristics, explain the rationale for the additional heuristics, and new methods need to be explored to aid the recall of the right heuristic, such as categorisation.
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.
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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".