724.3
Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups
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.