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This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment. Experiment Label: 20230523-VBCE Experiment design: Agents adapt ontologies to agree on decision taking. Introducing cultural values (independece, novelty, authority, mastery) that influence which agent adapts in case of interaction failure. Experiment setting: Agents learn decision trees (transformed into ontologies); get payoffs according to cultural values; adapt by splitting their leaf nodes Hypotheses: Positive mastery is needed for increasing accuracy. Negative independence causes the success rate to converge faster. Negative novelty increases ontology distance. Positive authority increases accuracy when used with positive mastery. Detailed information can be found in index.html or notebook.ipynb. [1] https://sake.re/20230523-VBCE [2] https://gitlab.inria.fr/moex/lazylav/
Cultural Evolution, Multi-agent Simulation
Cultural Evolution, Multi-agent Simulation
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |