Asset Management Through the Lens of Complex System Governance
Bibliographic record
Abstract
This paper examines the applicability of Complex System Governance to advance Asset Management. Asset management (AM) is increasing in importance as more societal serving systems are becoming dependent on the value of assets and their management. However, AM as a discipline lacks coherent grounding in systems theory --- a means for understanding the structure, behavior, and performance of complex systems. Complex System Governance (CSG) is focused on the design, execution, and evolution of system functions that provide for communications, control, coordination, and integration of complex systems, including assets. CSG focuses on the structure and order of complex systems through a rigorous grounding in systems theory (the axioms and propositions that govern the structure, behavior, and performance of complex systems), management cybernetics (the science of organizational structure), and system governance (focused on the provision of direction, oversight, and accountability). In this paper, the intersection of AM and CSG is explored concerning the value that can accrue to both fields through their intersection and joint development. The opportunities that lie at the intersection of these fields are examined. This paper concludes the exploration with a discussion of the implications for moving forward in bringing the value offered by CSG to the governance of assets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".