Predicting Team Performance from Communications in Simulated Search-and-Rescue
This work addresses team performance prediction for researchers or organizations using simulated environments, but it appears incremental as it builds on prior methods for inferring traits from behavioral data.
The paper tackled the problem of predicting team performance by analyzing conversational data from a Minecraft-based search-and-rescue simulation, finding that variations in outcomes can be explained through inferred traits and dynamics with different predictive power.
Understanding how individual traits influence team performance is valuable, but these traits are not always directly observable. Prior research has inferred traits like trust from behavioral data. We analyze conversational data to identify team traits and their correlation with teaming outcomes. Using transcripts from a Minecraft-based search-and-rescue experiment, we apply topic modeling and clustering to uncover key interaction patterns. Our findings show that variations in teaming outcomes can be explained through these inferences, with different levels of predictive power derived from individual traits and team dynamics.