Emergent engineering for the management of complex situations
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
Ubiquitous computing and communication environments connect systems and people in unprecedented ways, but also fundamentally challenge the mindset of traditional systems engineering. Complex techno-social systems exhibit spontaneous self-organization properties, based on decentralized interactions among a multitude of agents, that have preceded our ability as human designers to fully comprehend and control them. This should prompt us to steer away from managing details and, instead, focus on establishing the generic conditions for systems to develop and evolve under our guidance. In alignment with this paradigm shift we propose a methodological framework termed emergent engineering for deploying large-scale “eNetwork” systems, and illustrate it with self-organized security (SOS) scenarios. It involves an abstract model of programmable network self-construction in which nodes execute the same code, yet differentiate according to position. We illustrate these principles on a future application to SOS pointing to how this could lead to a new type of controllable self-organization, able to dynamically co-evolve the system with its environment.
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