@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} } @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} }