@article{,
title= {Semantic network mapping of religious material: testing multi-agent computer models of social theories against real-world data},
journal= {Cognitive Processing},
author= {Justin E. Lane},
year= {2015},
url= {https://link.springer.com/article/10.1007/s10339-015-0649-1},
abstract= {Agent-based modeling allows researchers to investigate theories of complex social phenomena and subsequently use the model to generate new hypotheses that can then be compared to real-world data. However, computer modeling has been underutilized in regard to the understanding of religious systems, which often require very complex theories with multiple interacting variables (Braxton et al. in Method Theory Study Relig 24(3):267–290, 2012. doi: 10.1163/157006812X635709; Lane in J Cogn Sci Relig 1(2):161–180, 2013). This paper presents an example of how computer modeling can be used to explore, test, and further understand religious systems, specifically looking at one prominent theory of religious ritual. The process is continuous: theory building, hypothesis generation, testing against real-world data, and improving the model. In this example, the output of an agent-based model of religious behavior is compared against real-world religious sermons and texts using semantic network analysis. It finds that most religious materials exhibit unique scale-free small-world properties and that a concept’s centrality in a religious schema best predicts its frequency of presentation. These results reveal that there adjustments need to be made to existing models of religious ritual systems and provide parameters for future models. The paper ends with a discussion of implications for a new multi-agent model of doctrinal ritual behaviors as well as propositions for further interdisciplinary research concerning the multi-agent modeling of religious ritual behaviors.

Torrent of academic paper https://link.springer.com/article/10.1007/s10339-015-0649-1},
keywords= {Multi-agent artificial intelligence Religion Semantic network Text analysis },
terms= {},
license= {},
superseded= {}
}