Computational Modeling of Complex Socio-Technical Systems - # 08-810/08-621

Instructor: Dr. Kathleen M. Carley
Units: 12.0

Course Description

We live and work in complex adaptive and evolving socio-technical systems. These systems may be complex for a variety of reasons. For example, they may be complex because there is a need to coordinate many groups, because humans are interacting with technology, because there are non routine or very knowledge intensive tasks, and so on. At the heart of this complexity is a set of adaptive agents who are connected or linked to other agents forming a network and who are constrained or enabled by the world they inhabit. Computational modeling can be used to help analyze, reason about, predict the behavior of, and possibly control such complex systems of "networked" agents.

This course is based on the simulation of complex socio-technical systems. This course teaches the student how to design, analyze, and evaluate such computational models. Students will: 1) gain experience with multiple types of computational modeling; 2) learn key similarities and differences between agent based modeling, system dynamic modeling, and machine learning models; 3) the relation of AI to simulation; 4) the role of data, both in the model creation and model validation phase; and 5) recent advances in modeling frameworks, validation, and testing.

Auxiliary Readings

Previously taught courses

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