COMPUTATIONAL SOCIAL SCIENCE

CSS SEMINAR - OCTOBER 9 - ORR

Friday, October 9, 3:00 p.m.
Center for Social Complexity Suite
Research Hall, Third Floor

Using the Networked Cognitive Systems Approach to Understand Human Information Diffusion

Mark Orr, Research Associate Professor
Social and Decision Analytics Laboratory
Virginia Tech-National Capital Region

ABSTRACT: A major barrier to increasing our scientific understanding of the processes that drive the spread of information in humans is the lack of programmatic, interdisciplinary work between the psychological and cognitive sciences, which focus on how individual people’s minds work, and the social sciences which focus on the social structures that constitute an individual’s social context (e.g., a social networks). This has resulted in little theoretical work that can apply to some of the difficult and deep questions about the social spread of information. In this discussion, I will offer a novel approach to this question. In particular, I will focus on a specific class of the spread of information in humans—the spread of attitudes and beliefs on social media networks via social influence (exposure to others’ beliefs can affect the beliefs of an individual). I will present a mixture of past research and upcoming projects that center around these types of questions: (1) to what degree does exposure to others’ beliefs via social media channels affect change in a person’s beliefs?, (2) how does the spread of beliefs operate over large sociotechnical networks? (3) what is the appropriate set of computational formalisms for representing the spread of beliefs on large sociotechnical networks.

Dr. Orr was originally trained as a cognitive psychologist at the University of Illinois at Chicago. He received augmentation to this training with postdoctoral fellowships in computational modeling (Carnegie Mellon), neuroscience (Albert Einstein College of Medicine), and epidemiology/complex systems (Columbia University). Over the past decade, he has become heavily involved in understanding dynamic processes and drivers of risky behavior and decision making, primarily in a public health context, at the scale of the individual and populations. He is now currently expanding these ideas into other contexts and for other applications (e.g., DoD, DoE, DHS).