Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups

Tuesday, 17 July 2018: 09:10
Oral Presentation
Jesse HOEY, University of Waterloo, Canada
Mei NAGAPPAN, University of Waterloo, Canada
Kimberly ROGERS, Dartmouth College, USA
Tobias SCHROEDER, Potsdam University of Applied Sciences, Germany
Technological and social innovations are increasingly generated through informal, distributed processes of collaboration, rather than in formal, hierarchical organizations. We will present research that uses a data-driven approach to explore the social and psychological mechanisms motivating self-organized collaborations, in which people come together to work on a common problem, without prompting by a third party. We focus on the example of open, collaborative software development in online collaborative networks like GitHub (github.com). Our research is based in affect control theory (ACT) and a recent probabilistic generalization of the theory known as Bayesian affect control theory (BayesACT). The general assumption of BayesACT 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.

BayesACT models human interactions as a partially observable Markov decision process, which captures the complexities of dynamic (temporal) decision sequences, and finds optimal solutions to complex decision problems. It makes explicit predictions about online interactions in a collaborative group, based on the notion of each group member holds an identity that is learnable, mathematically describable, and complementary to those of other group members. BayesACT applies insights from Bayesian probability theory to explain how people learn and adjust meanings through social experience, and show how stable interaction dynamics can emerge from individuals’ uncertain and noisy perceptions of their own and others’ identities. We will present work that (1) shows that identity dynamics explain how and why actors pursue particular goals in their interactions, and (2) offers a mathematically precise model for predicting and testing collaborative dynamics.