Rand     
William Rand
Assistant Professor & Director, Center for Complexity in Business
The Robert H. Smith School of Business
University of Maryland
"Using Trace Data, Social Networks, and Agent-Based
Modeling to Understand Information Diffusion"

Monday, February 22, 4:00 PM
Packard Lab room 466

Abstract:  With the increasing abundance of `digital footprints' left by human interactions in online environments, e.g., social media and app use, the ability to model human behavior at a detailed level has become increasingly possible. Many approaches have been proposed, however, most previous model frameworks are fairly restrictive, and often the models are not directly compared on a diverse collection of human behavior. We will explore a new modeling approach that enables the creation of models directly from data with few restrictions on the data. We will explore the application of this method to understanding information diffusion on social media, with applications for marketers. We will also examine using the same modeling framework to understand how to attribute the influence of different marketing channels to a resultant purchase. This work illustrates the power and usefulness of an individual-level approach to modeling and understanding large datasets.

Bio:  William Rand examines the use of computational modeling techniques, like agent-based modeling, geographic information systems, social network analysis, and machine learning, to help understand and analyze complex systems, such as the diffusion of innovation, organizational learning, and economic markets. He serves as the Director of the Center for Complexity in Business, the first academic research center focused solely on the application of complex systems techniques to business applications and management science. He also has an appointment with the University of Maryland Institute for Advanced Computer Studies, and affiliate appointments with the Departments of Decision, Operations & Information Technology and Computer Science.

He received his doctorate in Computer Science from the University of Michigan in 2005 where he worked on the application of evolutionary computation techniques to dynamic environments, and was a regular member of the Center for the Study of Complex Systems, where he built a large-scale agent-based model of suburban sprawl. Before coming to Maryland, he was awarded a postdoctoral research fellowship at Northwestern University in the Northwestern Institute on Complex Systems (NICO), where he worked with the NetLogo development team studying agent-based modeling, evolutionary computation and network science. Over the course of his research experience, he has used computer models to help understand a large variety of complex systems, such as the evolution of cooperation, suburban sprawl, traffic patterns, financial systems, land-use and land-change in urban systems, and many other phenomena. He has recently received research awards from Google / WPP, the National Science Foundation and the Marketing Science Institute.

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