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We review Affect Control Theory (ACT), a mathematically formalized theory that integrates sociological insights about the symbolic construction of the social order with psychological knowledge about cognitive-affective mechanisms, as a basis for equipping computational agents in social simulations with a sense of sociality. After explaining theoretical foundations and describing previous applications of ACT at the dyadic and group level, we describe a case study from an ongoing research project aimed at understanding self-organized online collaboration in software development with ACT-based social simulations. 

This study offers the first investigation on the normative processes through which Chinese form impressions of others in social interaction. Using affect control theory and its archived sentiment data from China, I estimate the Chinese impression formation models with a new Bayesian method. I then compare the Chinese models to the impression formation dynamics in U.S. English. Results show cross-cultural commonality in the affective processing of cultural concepts, with determinants of impression formation processes being largely universal. Findings also reveal two cultural variations that align with patterns uncovered by comparative cross-cultural research: 1) the Chinese models show less rigidity in the definition of situation; and 2) across two cultural models, the balance term has opposite effects on actor and behavior evaluation. To explore the implications of the impression models, I present a series of simulations, illustrating the predictive power of affect control theory as well as the impact of different cultural rules on social interaction. 

Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups (THEMIS.COG) was an interdisciplinary research collaboration of computer scientists and social scientists from the University of Waterloo (Canada), Potsdam University of Applied Sciences (Germany), and Dartmouth College (USA). This white paper summarizes the results of our research at the end of the grant term. Funded by the Trans-Atlantic Platform’s Digging Into Data initiative, the project aimed at theoretical and empirical modeling of identity and sentiments in collaborative groups. Understanding the social forces behind self-organized collaboration is important because technological and social innovations are increasingly generated through informal, distributed processes of collaboration, rather than in formal organizational hierarchies or through market forces. Our work used a data-driven approach to explore the social psychological mechanisms that motivate such collaborations and determine their success or failure. We focused on the example of GitHub, the world’s current largest digital platform for open, collaborative software development. In contrast to most, purely inductive contemporary approaches leveraging computational techniques for social science, THEMIS.COG followed a deductive, theory-driven approach. We capitalized on affect control theory, a mathematically formalized theory of symbolic interaction originated by sociologist David R. Heise and further advanced in previous work by some of the THEMIS.COG collaborators, among others. Affect control theory states that people control their social behaviours by intuitively attempting to verify culturally shared feelings about identities, social roles, and behaviour settings. From this principle, implemented in computational simulation models, precise predictions about group dynamics can be derived. It was the goal of THEMIS.COG to adapt and apply this approach to study the GitHub collaboration ecosystem through a symbolic interactionist lens. The project contributed substantially to the novel endeavor of theory development in social science based on large amounts of naturally occurring digital data. 

The computational modeling of groups requires models that connect micro-level with macro-level processes and outcomes. Recent research in computational social science has started from simple models of human behavior, and attempted to link to social structures. However, these models make simplifying assumptions about human understanding of culture that are often not realistic and may be limiting in their generality. In this paper, we present work on Bayesian affect control theory as a more comprehensive, yet highly parsimonious model that integrates artificial intelligence, social psychology, and emotions into a single predictive model of human activities in groups. We illustrate these developments with examples from an ongoing research project aimed at computational analysis of virtual software development teams.