Holonic stigmergy as a mechanism for engineering self-organizing applications.
Bibliographic record
Abstract
he word holon is made up of the Greek word “holos”, meaning whole, and the suffix “on”, suggesting a particle or part, and can thus be described as a part-whole. According to Koestler [1], this part-whole can be viewed as nodal point in at a certain level of a nested hierarchy (holarchy), describing the relationship between a set of dependant entities that are self-complete wholes and entities which are considered other dependent parts (located at lower levels in the holarchy). A holarchy, then, is a nested hierarchy of holons (Fig. 1 [14]), and, e.g. according to the Holonic Manufacturing Systems Consortium [2], is a system of holons that can cooperate to achieve a goal or objective. The holarchy defines the basic rules for cooperation of the holons and thereby limits their autonomy. Extensive work on self-organising holarchies applied to various domains has been done by Ulieru [10, 11]. In this paper we extend the holarchic self-organising model introduced by Ulieru [12] by adding to it stigmergic capabilities, which will further increase the flexibility of the emergence model by eliminating the need for a mediator holon (which encapsulates the optimal clustering mechanism in Ulieru’s work [13]) through the power of swarm intelligence optimal clustering.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".