TY - JOUR A1 - Hoey, Jesse A1 - Schröder, Tobias A1 - Morgan, Jonathan Howard A1 - Rogers, Kimberly B. A1 - Rishi, Deepak A1 - Nagappan, Meiyappan T1 - Artificial Intelligence and Social Simulation BT - Studying Group Dynamics on a Massive Scale JF - Small Group Research N2 - 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. KW - Künstliche Intelligenz KW - Gruppendynamik KW - Maschinelles Lernen KW - Virtuelles Team KW - Simulation Y1 - 2018 U6 - https://doi.org/10.1177/1046496418802362 SN - 1552-8278 VL - 49 IS - 6 SP - 647 EP - 683 PB - Sage Publications CY - London ER - TY - JOUR A1 - Zöller, Nikolas A1 - Morgan, Jonathan Howard A1 - Schröder, Tobias T1 - A topology of groups BT - What GitHub can tell us about online collaboration JF - Technological Forecasting and Social Change N2 - 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. KW - GitHub KW - Kollaboration KW - Netzwerkanalyse KW - Maschinelles Lernen Y1 - 2020 U6 - https://doi.org/10.1016/j.techfore.2020.120291 SN - 1873-5509 VL - 161 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Joseph, Kenneth A1 - Morgan, Jonathan Howard T1 - Friend or Foe: A Review and Synthesis of Computational Models of the Identity Labeling Problem JF - The Journal of Mathematical Sociology N2 - 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. KW - Soziale Identität KW - Computational social science KW - Analytische Soziologie Y1 - 2021 U6 - https://doi.org/10.1080/0022250X.2021.1923016 SN - 1545-5874 PB - Taylor and Francis CY - London ER - TY - CHAP A1 - Morgan, Jonathan Howard A1 - Zhao, Jun A1 - Zöller, Nikolas A1 - Sedlacek, Andrea A1 - Chen, Lena A1 - Piper, Hayley A1 - Beck, Yliana A1 - Rogers, Kimberly B. A1 - Hoey, Jesse A1 - Schröder, Tobias T1 - Modeling the Culture of Online Collaborative Groups with Affect Control Theory T2 - Advances in Social Simulation N2 - 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. KW - Culture KW - Affect KW - Gruppendynamik KW - Kollaboration Y1 - 2021 SN - 978-3-030-61503-1 U6 - https://doi.org/10.1007/978-3-030-61503-1_14 SP - 147 EP - 160 PB - Springer CY - Cham ER - TY - JOUR A1 - Luthardt, Jasmin A1 - Morgan, Jonathan Howard A1 - Bormann, Inka A1 - Schröder, Tobias T1 - Quantifying emotionally grounded discursive knowledge with cognitive-affective maps JF - Quality & Quantity - International Journal of Methodology N2 - 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 (N = 10), the second one was created based on given text data which the participants represented as a CAM following a standardized instruction manual (N = 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. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1007/s11135-021-01195-7 SN - 1573-7845 VL - 56 IS - 3 SP - 1557 EP - 1595 PB - Springer Science + Business Media B.V CY - Dordrecht ER - TY - JOUR A1 - Smith, Jeffrey A. A1 - Morgan, Jonathan Howard A1 - Moody, James T1 - Network sampling coverage III BT - Imputation of missing network data under different network and missing data conditions JF - Social Networks N2 - 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. KW - Missing data KW - Imputation KW - Network sampling KW - Network bias KW - Fehlende Daten KW - Imputationstechnik Y1 - 2022 U6 - https://doi.org/10.1016/j.socnet.2021.05.002 SN - 0378-8733 VL - 68 SP - 148 EP - 178 PB - Elsevier CY - Amsterdam ER -