Prof. Dr. Iris Lorscheid

Digital Business and Data Science B.Sc

Prof. Dr. Iris Lorscheid is the Vice-Rector of Research and head of the Digital Business and Data Science Program in Hamburg. She holds diploma degrees in Computer Science and Administrative Science, as well as a doctorate summa cum laude in Computer Science from the Hamburg University of Technology. Her dissertation was awarded as best dissertation by the Hamburg Volksbank Foundation in 2014. Prof. Lorscheid is an elected member of the management board of the European Social Simulation Association and part of the Scientific Advisory Board for the project Artificial Intelligence for Assessment, funded by the Volkswagen Foundation. She is also an editorial board member of the Journal of Artificial Societies and Social Simulation and reviewer in various established international scientific journals. Professor Lorscheid is an expert in the field of Data Science and Agent-based Simulation with a focus on the analysis of social systems and data-based theory-development.


(Ir-) Rationality of Teams: A Process-Oriented Model of Team Cognition Emergence


In today’s competitive environment, companies rely increasingly on teams and their flexibility. While effectively working teams may accomplish great results, ineffective teams may fall short of their potential and can even be a risk for the organization. Little is known about the socio-cognitive processes of team decisions and particularly the emergence of knowledge from individual to team level. This study addresses this process by analyzing team cognition as an emergent property. The here presented research approach allows for a deeper analysis of the underlying processes. A laboratory experiment provides information about quantitative patterns of individual and team cognition. For the analysis of these patterns, we introduce the team cognition matrix. By applying this format to the results of the laboratory experiment, this study identifies four categories of typical emergent team cognition structures. These four categories are the basis for a simple decision algorithm that was analyzed in an agent-based model. The resulting simulation shows that 67% of all simulated group decisions are very close to the empirical group decisions (ranking position distances ≤3) and 89% are close on a medium range (ranking position distances ≤6). The article contributes to the current literature by showing an innovative research approach that further is applied to open up the black box of successful team behavior beyond well-known static attributes.

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Published by Springer
2020, English
14 pages

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