TY - CHAP A1 - Ritter, Frank E. A1 - Morgan, Jonathan H. A1 - Kim, Jong W. T1 - Practical Advice on How to Run Human Behavioral Studies T2 - Proceedings of the 42nd Annual Conference of the Cognitive Science Society KW - Experimentelle Psychologie KW - Mensch-Maschine-Kommunikation Y1 - 2020 UR - https://cognitivesciencesociety.org/cogsci20/papers/0008/0008.pdf SP - 15 EP - 16 ER - TY - JOUR A1 - Zöller, Nikolas A1 - Morgan, Jonathan H. A1 - Schröder, Tobias T1 - Modeling interaction in collaborative groups BT - affect control within social structure JF - Journal of Artificial Societies and Social Simulation N2 - This paper studies the dynamics of identity and status management within groups in collaborative settings. We present an agent-based simulation model for group interaction rooted in social psychological theory. The model integrates affect control theory with networked interaction structures and sequential behavior protocols as they are often encountered in task groups. By expressing status hierarchy through network structure, we build a bridge between expectation states theory and affect control theory, and are able to reproduce central results from the expectation states research program in sociological social psychology. Furthermore, we demonstrate how the model can be applied to analyze specialized task groups or sub-cultural domains by combining it with empirical data sources. As an example, we simulate groups of open-source software developers and analyze how cultural expectations influence the occupancy of high status positions in these groups. KW - Affekt KW - Kontrolle KW - Kollaboration KW - Interaktion Y1 - 2021 U6 - https://doi.org/10.18564/jasss.4699 SN - 1460-7425 VL - 24 IS - 4 PB - Guildford CY - SimSoc Consortium ER - TY - RPRT A1 - Hoey, Jesse A1 - Nagappan, Meiyappan A1 - Rogers, Kimberly B. A1 - Schröder, Tobias A1 - Dametto, Diego A1 - De Zoysa, Nalin A1 - Iyer, Rahul A1 - Morgan, Jonathan H. A1 - Rishi, Deepak A1 - Sirianni, Antonio D. A1 - Yun, Seonghu A1 - Zhao, Jun A1 - Zöller, Nikolas T1 - Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups N2 - 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. KW - Computational social science KW - GitHub KW - Gruppendynamik KW - Sozialpsychologie Y1 - 2021 U6 - https://doi.org/10.31235/osf.io/4hukx SP - 1 EP - 36 ER - TY - JOUR A1 - Sirianni, Antonio D. A1 - Morgan, Jonathan H. A1 - Zöller, Nikolas A1 - Rogers, Kimberly B. A1 - Schröder, Tobias T1 - Complements and Competitors BT - The Co-functionality and Co-diffusion of Languages on a Collaborative Coding Platform N2 - 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. KW - Soziologie KW - Kommunikation Y1 - 2023 U6 - https://doi.org/10.31235/osf.io/bfmy2 SP - 1 EP - 23 ER - TY - JOUR A1 - Sirianni, Antonio D. A1 - Morgan, Jonathan H. A1 - Zöller, Nikolas A1 - Rogers, Kimberly B. A1 - Schröder, Tobias T1 - Complements and competitors BT - Examining technological co-diffusion and relatedness on a collaborative coding platform JF - PNAS nexus N2 - Diffusive and contagious processes spread in the context of one another in connected populations. Diffusions may be more likely to pass through portions of a network where compatible diffusions are already present. We examine this by incorporating the concept of “relatedness” from the economic complexity literature into a network co-diffusion model. Building on the “product space” concept used in this work, we consider technologies themselves as nodes in “product networks,” where edges define relationships between products. Specifically, coding languages on GitHub, an online platform for collaborative coding, are considered. From rates of language co-occurrence in coding projects, we calculate rates of functional cohesion and functional equivalence for each pair of languages. From rates of how individuals adopt and abandon coding languages over time, we calculate measures of complementary diffusion and substitutive diffusion for each pair of languages relative to one another. Consistent with the principle of relatedness, network regression techniques (MR-QAP) reveal strong evidence that functional cohesion positively predicts complementary diffusion. We also find limited evidence that functional equivalence predicts substitutive (competitive) diffusion. Results support the broader finding that functional dependencies between diffusive processes will dictate how said processes spread relative to one another across a population of potential adopters. KW - Computational social science KW - Innovation KW - Netzwerk Y1 - 2024 U6 - https://doi.org/10.1093/pnasnexus/pgae549 SN - 2752-6542 VL - 3 IS - 12 SP - 1 EP - 13 PB - Oxford University Press CY - Oxford ER -