Understanding Complexity-Research Applications for Policy & Political Science
Wednesday, September 2, 1:30-5:30 p.m.
Hilton Union Square 1&2
Complexity science and the study of complex systems focus on how independent parts interact with the environment, giving rise to aggregate system behavior. Complexity science allows social science researchers to move beyond the myth of isolated systems. Due to advances in computing, researchers can now account for novel and rare events, along with understanding trends and indirect impacts of system parts, whole, and interrelationships.
The goal of this session is to provide you with a basic understanding of systems and how they interact at the local and global level, along with updates on the latest techniques in modeling. Concepts like agents and how they interact with the environment in simple, complex, adaptive, and emergent systems will be presented in the form of political science and policy simulations. Sample political science and policy problems will be presented and broken down into theory and assumptions, in order to expand skills required for a successful simulation modeling. Strategies behind identifying appropriate agent types, behaviors, and attributes, along with establishing system rules, will be presented for model inclusion. The role of energy, learning, feedback, information, scaling, and fitness will be discussed in order to add to modeling dynamics.
Upon completion of this course, participants will be able to better understand how to incorporate complexity theory into research and recognize the significance of pattern formation in social phenomena. Additionally, participants will be supplied with the base knowledge to move into developing agent-based models and complexity simulations for political science and policy research. An in-depth course curriculum guide will be supplied for additional information and resources on complexity.