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Predicting the Performance in Decision-Making Tasks: From Individual Cues to Group Interaction

This paper addresses the problem of predicting the performance of decision-making groups. Towards this goal, we evaluate the predictive power of group attributes and discussion dynamics by using automatically extracted features, such as group members' aural and visual cues, interaction between team members, and influence of each team member, as well as self-reported features such as personality- and perception-related cues, hierarchical structure of the group, and individual- and group-level task performances. We tackle the inference problem from two angles depending on the way that features are extracted: 1) a holistic approach based on the entire meeting, and 2) a sequential approach based on the thin slices of the meeting. In the former, key factors affecting the group performance are identified and the prediction is achieved by support vector machines. As for the latter, we compare and contrast the classification performance of an influence model-based novel classifier with that of hidden Markov model (HMM). Experimental results indicate that the group looking cues and the influence cues are major predictors of group performance and the influence model outperforms the HMM in almost all experimental conditions. We also show that combining classifiers covering unique aspects of data results in improvement in the classification performance.