Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale

[We’re pleased to welcome authors, Jesse Hoey of the University of Waterloo, Tobias Schröder of Potsdam University of Applied Sciences, Jonathan Morgan of Potsdam University of Applied Sciences, Kimberly B. Rogers of Dartmouth College, Deepak Rishi of the University of Waterloo, and Meiyappan Nagappan of the University of Waterloo. They recently published an article in Small Group Research entitled “Spotlight on Methods: Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale,” which is currently free to read for a limited time. Below, They discusses some of the findings of this research:]

SGR_72ppiRGB_powerpointTechnological and social innovations are increasingly generated through informal, distributed processes of collaboration, rather than in formal, hierarchical organizations. In this article, we present a novel combination of data-driven and model-based approaches to explore the social and psychological mechanisms motivating these modern self-organized collaborations. We focus on the example of open, collaborative software development in online collaborative networks like GitHub (github.com). The synthesized approach is based in affect control theory (ACT), and a recent framing in Artificial Intelligence known as Bayesian affect control theory (BayesACT). The general assumption of ACT is that humans are motivated in their social interactions by affective alignment: They strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general worldviews as constructed through culturally shared symbols. This alignment is used in BayesACT as a control mechanism to generate artificially intelligent agents that can learn to be functioning members of a social order (see bayesact.ca for further information).

We show in this article how such a model solves two basic problems in the social scientific study of groups and teams. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). The ACT-based models we present allow for sophisticated machine learning techniques to be combined in a parsimonious way with validated social-psychological models of group behaviour such that both of these problems are solved in a single computational model.

The purpose of the present article is to discuss the promises of this cross-disciplinary, computational approach to the study of small group dynamics. We review computational methods for using large amounts of social media data, and connect these methods to theoretically informed models of human behaviour in groups. To use a metaphor, we are digging into digital group dynamics data with a sophisticated, artificially intelligent shovel, and showing how computational social science can be taken to a new level with this unique and novel combination of data-driven and model-based approaches. The work is an international collaboration called THEMIS.COG (themis-cog.ca) between researchers in Canada (University of Waterloo), the USA (Dartmouth College), and Germany (Potsdam University of Applied Sciences).

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This entry was posted in Groups, Organizational Behavior, Organizational Research, Organizational Studies, Personality, Relationships, Small Group Research, Teams and tagged , , , , by Cynthia Nalevanko, Senior Editor, SAGE Publishing. Bookmark the permalink.

About Cynthia Nalevanko, Senior Editor, SAGE Publishing

Founded in 1965, SAGE is the world’s leading independent academic and professional publisher. Known for our commitment to quality and innovation, SAGE has helped inform and educate a global community of scholars, practitioners, researchers, and students across a broad range of subject areas. With over 1500 employees globally from principal offices in Los Angeles, London, New Delhi, Singapore, Washington DC, and Melburne, our publishing program includes more than 1000 journals and over 900 books, reference works and databases a year in business, humanities, social sciences, science, technology and medicine. Believing passionately that engaged scholarship lies at the heart of any healthy society and that education is intrinsically valuable, SAGE aims to be the world’s leading independent academic and professional publisher. This means playing a creative role in society by disseminating teaching and research on a global scale, the cornerstones of which are good, long-term relationships, a focus on our markets, and an ability to combine quality and innovation. Leading authors, editors and societies should feel that SAGE is their natural home: we believe in meeting the range of their needs, and in publishing the best of their work. We are a growing company, and our financial success comes from thinking creatively about our markets and actively responding to the needs of our customers.

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