The Holonic Enterprise and Theory Emergence: On emergent features of self-organization in distributed virtual agents.
Bibliographic record
Abstract
In this paper we explore the holonic enterprise as an example of the phenomenon of emergence. To conduct this exploration, we describe some of the development work now being carried out in the holonic enterprise field and examine anticipated near-future outcomes of this work. We then provide a brief overview of significant achievements and innovations such as the Copernican revolution, the development of the calculus, and the explication of conceptual analysis as a methodological consequence of the demise of the logicism project, for example. We briefly discuss the paradigmshifting consequences of these achievements, suggesting that the holonic enterprise development work discussed in this paper as a phenomenon of emergence may have distinct parallels with such well-known innovation events. Developing a framework of 1st, 2nd and 3rd order conceptual analysis based on the demands and circumstances of unfolding technical, political and conceptual change, we then explore some implications of paradigm shifts in general, and speculate on the nature of the holonic enterprise in light of that overview. We suggest that a new 4th order of conceptual analysis may well be necessary to fully understand and embrace the concept of emergence, and finally conclude that the technical and conceptual work being done to create the holonic enterprise necessarily addresses theory emergence.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".