Some Thoughts on Unprecedented Conceptual Challenges Presented by Holonic Multi-Agent Systems
Bibliographic record
Abstract
The unprecedented complexity of challenges arising from our emerging technologies has outstripped the very conceptual skills we have used to invent them. This is illustrated with the emergence of autonomous holonic multi-agent systems. It is difficult to imagine that the societal, economic, scientific, environmental, and knowledge consequences of such complex emerging technologies could be other than profound. Standard technical and political responses to such unprecedented challenges are helpful but do not by themselves lead to solution. This paper therefore begins the task of exploring how conceptual skills might be enhanced to effectively deal with anticipated consequences that cannot be fully known. Hopefully this provides a plausible foundation for defining new opportunities arising from these circumstances, and perhaps provides us with improved pre-adaptive capacities to assist in dealing with technologies that emerges in years to come.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| 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".