A Systems Engineering Framework for Navigating Complexity
Bibliographic record
Abstract
Abstract The Cynefin Framework is a popular framework primarily designed to help leaders make strategic decisions in complex systems. Its popularity can be associated with the simplification of many complexity concepts into an accessible framework. It uses simple, complicated, complex and chaotic terms as its foundation. The INCOSE Complex Systems Working Group has been working on Complexity understanding and how it relates to Systems Engineers since 2006. This work has led to the evolution of these same terms within INCOSE. This paper explores the structure of the Cynefin framework and sees if that structure can be used with the most recently defined terms to develop a new tool that is more relevant to Systems Engineers in navigating complexity. This work led to the development of the Pleko framework. Testing of the framework indicates that the Pleko framework is useful for removing unnecessary complexity. Further, the Pleko Framework was compared to the COSYSMO model, which uses similar axes but was developed independently. This enables the two models to be combined, suggesting that the cost and benefits of complexity mitigation strategies, should they be required, can be estimated to inform decision‐making on how best to proceed.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".