A hybrid agent based virtual organization for studying knowledge evolution in social systems
Bibliographic record
Abstract
Social modeling applies computational methods and techniques to the analysis of social processes and human behavior.Cultural algorithms (CA’s) are evolutionary systems which utilize agent technology and which supports any evolutionarystrategy like genetic algorithm, evolutionary algorithm or swarm intelligence or ant algorithms. CA’s have been used formodeling the evolution of complex social systems, for re-engineering rule based systems, for data mining, and for solvingoptimization problems. In the current study a cultural algorithm framework is used to model an Agent Based VirtualOrganization (ABVO) for studying the dynamics of a social system at micro as well as macro level. Research gap exists indefining a concrete and systematic method for evaluating and validating Agent Based Social Systems (ABSS). Also theknowledge evolution process at micro and macro levels of an organization needs further exploration. The proposed CA isapplied to the problem of multi-objective optimization (MOO) of classification rules. The evolutionary knowledgeproduced by the agents in creating the rules is accepted into the belief space of the CA and macro evolution takes place.The belief space in turn influences the agents in successive generations. The rules created by the individuals and theknowledge sources created during evolution provide a concrete method to evaluate both the individuals as well as thewhole social system. The feasibility of the system has been tested on bench mark data sets and the results are encouraging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".