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Drawing on Bayesian probability theory, we propose a generalization of affect control theory (BayesACT) that better accounts for the dynamic fluctuation of identity meanings for self and other during interactions, elucidates how people infer and adjust meanings through social experience, and shows how stable patterns of interaction can emerge from individuals’ uncertain perceptions of identities. Using simulations, we illustrate how this generalization offers a resolution to several issues of theoretical significance within sociology and social psychology by balancing cultural consensus with individual deviations from shared meanings, balancing meaning verification with the learning processes reflective of change, and accounting for noise in communicating identity. We also show how the model speaks to debates about core features of the self, which can be understood as stable and yet malleable, coherent and yet composed of multiple identities that may carry competing meanings. We discuss applications of the model in different areas of sociology, implications for understanding identity and social interaction, as well as the theoretical grounding of computational models of social behavior.
Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. 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). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams.
We review Affect Control Theory (ACT) as a promising basis for equipping computational agents in social simulations with a sense of sociality. ACT is a computational theory that integrates sociological insights about the symbolic construction of the social order with psychological knowledge about cognitive-affective mechanisms. After explaining the theoretical foundations of ACT and applications of the theory at the dyadic and group level, we describe a case study applying the theory from an ongoing research project examining self-organized online collaboration in software development.
Author Reply
(2014)
We are pleased that all the commentators seem to agree that a theory-driven integration across disciplines is a worthwhile endeavor to better understand the social constitution of emotion. In our reply, we first take up the idea of relating affect control theory (ACT) to cultural priming and suggest links to an ACT-inspired constraint satisfaction explanation of priming. Second, we address reservations concerning ACT’s capability to account for emotions with nonconceptual content and to explain stability and change in affective meanings. Third, we clarify the relation of affect control theory to psychological constructionism, in particular with regard to conceptualizations of culture and society. Finally, we suggest that computational models are an adequate tool to address multilevel issues in the study of emotion.
In recent years, scholars have come to understand emotions as dynamic and socially constructed—the product of interdependent cultural, relational, situational, and biological influences. While researchers have called for a multilevel theory of emotion construction, any progress toward such a theory must overcome the fragmentation of relevant research across various disciplines and theoretical frameworks. We present affect control theory as a launching point for cross-disciplinary collaboration because of its empirically grounded conceptualization of social mechanisms operating at the interaction, relationship, and cultural levels, and its specification of processes linking social and individual aspects of emotion. After introducing the theory, we illustrate its correspondence with major theories of emotion construction framed at each of four analytical levels: cultural, interactional, individual, and neural.
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.
Complements and Competitors
(2023)
The vast majority of research on the diffusion of innovations focuses on how one particular idea or technology spreads across a network of connected individuals, or a population more generally. Diffusive processes are typically considered either in isolation. We consider innovations themselves as nodes in a larger network, and look at how individual innovations can spread with or against one another in a larger community of potential adopters. Using a large publicly-available data set, custom-designed measurements, and network analysis methods, we present a temporal analysis of a set of technological innovations. Specifically, we examine how coding languages spread across users on GitHub, an online platform for collaborative coding. By looking at which languages co-appear in individual projects, we develop network based measurements of how frequently languages appear together in the same coding projects (functional cohesion), and how similar two languages are in terms of how often they co-appear with other languages in coding projects (functional equivalence). We also assess two types of diffusion, one form where users of one language become users of an additional language (complementary or 'piggybacking' diffusion), and another where users of one language abandon one language for another (competitive or 'cannibalistic' diffusion). Using MR-QAP Regression Techniques, we find strong evidence that functional cohesion positively predicts complementary diffusion. We also find some evidence that functional equivalence predicts competitive diffusion. More broadly, we find support for the idea that the networks of functional relationships between innovations is important for understanding diffusive processes.