Predictive Algorithm for Team Mental Model Convergence - Poozhithara, Jeffy Jahfar; Kennedy, Deanna M.; Onstot, Spencer; Januskeviciute, Agne; Cekrezi, Marjanthi (IEEE transactions on computational social systems, 2023)
Is everyone on your team on the same page about the task? This is a question team leaders want to know. Herein, we take an approach that can help managers move the team in the right direction of team mental models (TMMs), individually held cognitive representations of task components that when similar and/or accurate can promote task success. We begin by defining the new concept of mental model shifts (MMSs) as the directional shift in each team member's mental model, leading to an increase or decrease in convergence toward a shared or quality referent mental model. Next, we propose an algorithm that applies the concepts of Markov chains and vector geometry on communication patterns to predict future patterns and TMM convergence (i.e., sharedness and quality) levels. We base the model on a dataset of teams conducting the National Aeronautics and Space Administration (NASA) human exploration research analog (HERA) missions from which we draw communication attributes of MMS, process topic, and message purpose. We show that tasks can be modeled as vectors using the frequency of attribute patterns. Our initial experiments show that an accuracy of up to 86.21% can be achieved in predicting future communication patterns with data from real-world tasks. Furthermore, we validate the estimation of TMM sharedness, showing that the model results are comparable to sharedness ratings provided by subject matter experts with an average accuracy of 71.29%. Research and practical implications are discussed.