Enterprise Decision-Making in System-of-Systems Considering Different Levels of Centralized Management
Bibliographic record
Abstract
Systems are increasingly deployed in environments where they interact with other systems to deliver enhanced and integrated capabilities. Together, these systems are referred to as system-of-systems. While the benefits of a system-of-systems can lie in the emergent behavior that the systems produce together, the challenges that arise when developing and evolving a system-ofsystems are complex. Each constituent system - of which the system-of-systems is composed - may be at a different stage in its lifecycle and owned or managed by a separate enterprise. These enterprises must make independent decisions about the acquisition, development, or use of their systems. In some cases, a central enterprise may hold decision-making authority over multiple constituent systems, but this is not always the case. This paper examines the role of multiple enterprises in decision-making within a system-of-systems, identifies critical gaps, and synthesizes these insights to outline a direction towards a system-of-systems multi-enterprise collaboration.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".