@incollection{Quedenbaum, author = {Quedenbaum, Peggy}, title = {Mit Mut und Liebe : Eine Jugend im Kampf gegen Ungerechtigkeit und Terror. Gisela Konopka}, series = {Bibliothek der Gr{\"u}nderinnen : Bibliographische Ann{\"a}herungen an die Gr{\"u}nderinnen und Gr{\"u}nder der professionellen Sozialarbeit / hrsg. von Helmut Kn{\"u}ppel. Bearb. von Ulf Damerow. - Potsdam: Fachhochsch., 2005. - 330 S.}, booktitle = {Bibliothek der Gr{\"u}nderinnen : Bibliographische Ann{\"a}herungen an die Gr{\"u}nderinnen und Gr{\"u}nder der professionellen Sozialarbeit / hrsg. von Helmut Kn{\"u}ppel. Bearb. von Ulf Damerow. - Potsdam: Fachhochsch., 2005. - 330 S.}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:525-3101}, publisher = {Fachhochschule Potsdam}, abstract = {Gisela wurde stark von Gertrude Willson beeinflusst, die zur Entwicklung der Sozialarbeit im umfassenden Amerikanischen Sinne beigetragen hatte, sowie von zwei weiteren Emigranten, dem {\"o}sterreichischen Vertreter der Psychoanalytischenp{\"a}dagogik, Fritz Redl und dem „Vater" der Gruppendynamik Kurt Lewin. Ihre eigenen Ausf{\"u}hrungen zum „social group work" sind gepr{\"a}gt von ihren Gruppenerfahrungen in der deutschen Jugend- und Arbeiterbewegung sowie vom Reformp{\"a}dagogischen Erbe.}, subject = {Konopka, Gisela}, language = {de} } @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} } @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} }