@article{HoeySchroederMorganetal., author = {Hoey, Jesse and Schr{\"o}der, Tobias and Morgan, Jonathan Howard and Rogers, Kimberly B. and Rishi, Deepak and Nagappan, Meiyappan}, title = {Artificial Intelligence and Social Simulation}, series = {Small Group Research}, volume = {49}, journal = {Small Group Research}, number = {6}, publisher = {Sage Publications}, address = {London}, issn = {1552-8278}, doi = {10.1177/1046496418802362}, pages = {647 -- 683}, abstract = {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.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{JosephMorgan, author = {Joseph, Kenneth and Morgan, Jonathan Howard}, title = {Friend or Foe: A Review and Synthesis of Computational Models of the Identity Labeling Problem}, series = {The Journal of Mathematical Sociology}, journal = {The Journal of Mathematical Sociology}, publisher = {Taylor and Francis}, address = {London}, issn = {1545-5874}, doi = {10.1080/0022250X.2021.1923016}, abstract = {We introduce the identity labeling problem - given an individual in a social situation, can we predict what identity(ies) they will be labeled with by someone else? This problem remains a theoretical gap and methodological challenge, evidenced by the fact that models of social-cognition often sidestep the issue by treating identities as already known. We build on insights from existing models to develop a new framework, entitled Latent Cognitive Social Spaces, that can incorporate multiple social cues including sentiment information, socio-demographic characteristics, and institutional associations to estimate the most culturally expected identity. We apply our model to data collected in two vignette experiments, finding that it predicts identity labeling choices of participants with a mean absolute error of 10.9\%, a 100\% improvement over previous models based on parallel constraint satisfaction and affect control theory.}, subject = {Soziale Identit{\"a}t}, language = {en} } @inproceedings{MorganZhaoZoelleretal., author = {Morgan, Jonathan Howard and Zhao, Jun and Z{\"o}ller, Nikolas and Sedlacek, Andrea and Chen, Lena and Piper, Hayley and Beck, Yliana and Rogers, Kimberly B. and Hoey, Jesse and Schr{\"o}der, Tobias}, title = {Modeling the Culture of Online Collaborative Groups with Affect Control Theory}, series = {Advances in Social Simulation}, booktitle = {Advances in Social Simulation}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-61503-1}, doi = {10.1007/978-3-030-61503-1_14}, pages = {147 -- 160}, abstract = {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.}, subject = {Gruppendynamik}, language = {en} } @article{ZoellerMorganSchroeder, author = {Z{\"o}ller, Nikolas and Morgan, Jonathan Howard and Schr{\"o}der, Tobias}, title = {A topology of groups}, series = {Technological Forecasting and Social Change}, volume = {161}, journal = {Technological Forecasting and Social Change}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1873-5509}, doi = {10.1016/j.techfore.2020.120291}, pages = {19}, abstract = {In this work, we study the collaboration patterns of open source software projects on GitHub by analyzing the pull request submissions and acceptances of repositories. We develop a group typology based on the structural properties of the corresponding directed graphs, and analyze how the topology is connected to the repositorys collective identity, hierarchy, productivity, popularity, resilience and stability. These analyses indicate significant differences between group types and thereby provide valuable insights on how to effectively organize collaborative software development. Identifying the mechanisms that underlie self-organized collaboration on digital platforms is important not just to better understand open source software development but also all other decentralized and digital work environments, a setting widely regarded as a key feature of the future work place.}, subject = {GitHub}, language = {en} } @article{ZoellerMorganSchroeder, author = {Z{\"o}ller, Nikolas and Morgan, Jonathan H. and Schr{\"o}der, Tobias}, title = {Modeling interaction in collaborative groups}, series = {Journal of Artificial Societies and Social Simulation}, volume = {24}, journal = {Journal of Artificial Societies and Social Simulation}, number = {4}, publisher = {Guildford}, address = {SimSoc Consortium}, issn = {1460-7425}, doi = {10.18564/jasss.4699}, pages = {25}, abstract = {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.}, subject = {Affekt}, language = {en} } @inproceedings{RitterMorganKim, author = {Ritter, Frank E. and Morgan, Jonathan H. and Kim, Jong W.}, title = {Practical Advice on How to Run Human Behavioral Studies}, series = {Proceedings of the 42nd Annual Conference of the Cognitive Science Society}, booktitle = {Proceedings of the 42nd Annual Conference of the Cognitive Science Society}, organization = {Cognitive Science Society}, pages = {15 -- 16}, subject = {Experimentelle Psychologie}, language = {en} } @article{LuthardtMorganBormannetal., author = {Luthardt, Jasmin and Morgan, Jonathan Howard and Bormann, Inka and Schr{\"o}der, Tobias}, title = {Quantifying emotionally grounded discursive knowledge with cognitive-affective maps}, series = {Quality \& Quantity - International Journal of Methodology}, volume = {56}, journal = {Quality \& Quantity - International Journal of Methodology}, number = {3}, publisher = {Springer Science + Business Media B.V}, address = {Dordrecht}, issn = {1573-7845}, doi = {https://doi.org/10.1007/s11135-021-01195-7}, pages = {1557 -- 1595}, abstract = {Belief systems matter for all kinds of human social interaction. People have individual cognitions and feelings concerning processes in their environment, which is why they may evaluate them differently. Belief systems can be visualized with cognitive-affective maps (CAMs; as reported by Thagard (in: McGregor (ed) EMPATHICA: A computer support system with visual representations for cognitive-affective mapping, AAAI Press, CA, 2010)). However, it is unclear whether CAMs can be constructed in an intersubjective way by different researchers attempting to map the beliefs of a third party based on qualitative text data. To scrutinize this question, we combined qualitative strategies and quantitative methods of text and network analysis in a case study examining belief networks about participation. Our data set consists of 10 sets of two empirical CAMs: the first CAM was created based on participants' freely associating concepts related to participation in education (Nā€‰=ā€‰10), the second one was created based on given text data which the participants represented as a CAM following a standardized instruction manual (Nā€‰=ā€‰10). Both CAM-types were compared along three dimensions of similarity (network similarity, concept association similarity, affective similarity). On all dimensions of similarity, there was substantially higher intersubjective agreement in the text-based CAMs than in the free CAMs, supporting the viability of cognitive affective mapping as an intersubjective research method for studying the emotional coherence of belief systems and discursive knowledge. In addition, this study highlights the potential for identifying group-level differences based on how participants associate concepts.}, language = {en} } @techreport{HoeyNagappanRogersetal., author = {Hoey, Jesse and Nagappan, Meiyappan and Rogers, Kimberly B. and Schr{\"o}der, Tobias and Dametto, Diego and De Zoysa, Nalin and Iyer, Rahul and Morgan, Jonathan H. and Rishi, Deepak and Sirianni, Antonio D. and Yun, Seonghu and Zhao, Jun and Z{\"o}ller, Nikolas}, title = {Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups}, doi = {10.31235/osf.io/4hukx}, pages = {1 -- 36}, abstract = {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.}, subject = {Computational social science}, language = {en} } @article{SmithMorganMoody, author = {Smith, Jeffrey A. and Morgan, Jonathan Howard and Moody, James}, title = {Network sampling coverage III}, series = {Social Networks}, volume = {68}, journal = {Social Networks}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0378-8733}, doi = {10.1016/j.socnet.2021.05.002}, pages = {148 -- 178}, abstract = {Missing data is a common, difficult problem for network studies. Unfortunately, there are few clear guidelines about what a researcher should do when faced with incomplete information. We take up this problem in the third paper of a three-paper series on missing network data. Here, we compare the performance of different imputation methods across a wide range of circumstances characterized in terms of measures, networks and missing data types. We consider a number of imputation methods, going from simple imputation to more complex model-based approaches. Overall, we find that listwise deletion is almost always the worst option, while choosing the best strategy can be difficult, as it depends on the type of missing data, the type of network and the measure of interest. We end the paper by offering a set of practical outputs that researchers can use to identify the best imputation choice for their particular research setting.}, subject = {Fehlende Daten}, language = {en} } @incollection{JungMorganHoeyetal., author = {Jung, Joshua and Morgan, Jonathan and Hoey, Jesse and Schr{\"o}der, Tobias and Wolf, Ingo}, title = {Grounding social interaction with affective intelligence}, series = {Advances in Artificial Intelligence : 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings}, booktitle = {Advances in Artificial Intelligence : 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, Victoria, BC, Canada, May 31 - June 3, 2016. Proceedings}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-34111-8}, doi = {10.1007/978-3-319-34111-8_7}, pages = {52 -- 57}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{SirianniMorganZoelleretal., author = {Sirianni, Antonio D. and Morgan, Jonathan H. and Z{\"o}ller, Nikolas and Rogers, Kimberly B. and Schr{\"o}der, Tobias}, title = {Complements and Competitors}, doi = {10.31235/osf.io/bfmy2}, pages = {1 -- 23}, abstract = {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.}, subject = {Soziologie}, language = {en} }