Social Structure Simulation and Inference Using Artificial Intelligence Techniques

摘要

The study of complex social and technological systems, such as organizations, requires a sophisticated approach that accounts for the underlying psychological and sociological principles, communication patterns and the technologies within these systems. Social Network Analysis and link analysis have since inception operated on the cutting edge bringing together mathematical analysis of social structures and qualitative reasoning and interpretation. As available computing power grew, social network-based models have become not only an analysis tool, but also a methodology for building new theories of social behaviour and organizational evolution, frequently through the creation of simulation models. This work examines the past approaches of creating Social Network-based semantically consistent and interpretable models of social structure and social networks, as well as social simulation tools. I propose the creation of a multi-theory, multi-level simulation model of social structure that relies on social network theory and Artificial Intelligence algorithms. I further propose the creation of a robust and scalable social structure semantic that facilitates interpretable reasoning about evolution of social structure.