A model of distributed team information processing under ambiguity

Distributed information processing by a three-person hierarchical team, consisting of a primary decision maker (DM) and two expert subordinates, is considered. The problem context is binary hypotheses testing, wherein the team is asked to decide whether a contact is a threat or a neutral based on distributed, noisy, and at times ambiguous, measurements. A normative Bayesian model, which prescribes the behavior of an optimal team, is developed. The normative predictions are compared with the experimental data, and the cognitive biases of conservatism and of undervaluing of subordinates´ reports by the primary DM are identified. A normative-descriptive model incorporating these human biases is developed using Kalman filtering (least squares) theory. The output of the resulting normative-descriptive model is shown to provide an excellent match with the experimental data