THE EMERGENCE OF SOCIAL NETWORK HIERARCHY USING CULTURAL ALGORITHMS
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we extend the cultural framework previously developed for the Village multi-agent simulation in Swarm to include the emergence of a hub network from two base networks. The first base network is kinship, over which generalized reciprocal exchange is defined, and the second is the economic network where agents carry out balanced reciprocal exchange. Agents, or households, are able to procure several resources. We use Cultural Algorithms as a framework for the emergence of social intelligence at both individual and cultural levels. Successful agents in both networks can promote themselves to be included in the hub network where they can develop exchange links to other hubs. The collective effect of the hub network is representative of the quality of life in the population and serves as an indicator for motives behind the mysterious emigration from the region. Knowledge represents the development and use of exchange relationships between agents. The presence of defectors in the hub network improved resilience of the social system while maintaining the population size at that observed where no defectors were present.
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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 | 0.001 | 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