3-day workshop runs Sept 2nd 5-8pm, Sept 3rd 5-8pm, and Sept 4th 5:30-7:30pm.
Much quantitative social science and behavioral research has focused on identifying statistical relationships in cross-sectional data. While rigorous and tractable, this research typically assumes the objects of study are independent of one another, and thus assumes away the complex social processes that we hope to understand. Qualitative (ethnographic and comparative-historical) lenses have allowed us to view the social world as a web of interdependent and contingent processes, with macro-level cultures, communities and organizations emerging from and constraining the micro-level interactions of individuals, relationships and families. An explosion of recent work has used computer simulation to think systematically and rigorously about these complex social dynamics. Simulation research can offer rich, nuanced process models similar to qualitative work, but employs a rigorous, transparent and replicable framework that can be extended to other research contexts, similar to statistical approaches.
Theorists use computer models to elucidate, extend, integrate and validate social theory. Policy analysts use computer models to predict outcomes of policy scenarios in complex and interactive domains. Managers use computer models to design efficient and robust organizational operations and implement effective interventions. This proliferation of simulation work has generated great interest in computer modeling methods, but few disciplinary departments presently offer general training in this area. This introductory workshop will allow attendees to understand some of the overall goals and methods of social simulation, give them hands-on experience in experimenting on models, and point them to resources to begin using these tools in their own work.
We will explore a range of modeling domains including social networks, social influence, individual and social learning, and social norms. We will draw substantive applications mostly from the science of organizations (e.g. models of organizational culture and turnover, dynamics of collective action in teams, and intergroup conflict) and public health (e.g. understanding and resolving health disparities, transmission of disease on contact networks, social contagion and diffusion of health or risk behaviors).
James A. Kitts has recently joined UMass as an associate professor in the Department of Sociology and Director of the Computational Social Science Institute. He has previously held faculty appointments at Columbia Business School (Management), Dartmouth College (Sociology), and the University of Washington (Sociology). He has taught computational modeling Ph.D. seminars at Columbia, the University of Arizona, and the University of Washington, and has led modeling workshops for government agencies, universities and research institutes around the world. His research recently appeared in American Sociological Review, Social Forces, Social Psychology Quarterly, and Demography.