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.


Toward a better understanding of team decision processes: combining laboratory experiments with agent-based modeling


Despite advances in the field, we still know little about the socio-cognitive processes of team decisions, particularly their emergence from an individual level and transition to a team level. This study investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. First, the laboratory experiment generates data about individual and team cognition structures. Then, the agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions. Overall, we contribute to the current literature by presenting an innovative mixed-method approach that opens and exposes the black box of team decision processes beyond well-known static attributes.

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Published by Springer
2021, English
37 pages

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