Distributed information processing by a three-person team operating in a binary hypothesis-testing environment is considered. The team is hierarchical, with a primary decision-maker (DM0) and two subordinate DMs (DM1 and DM2). Given a set of measurements, the team has to decide whether a contact is a threat or a neutral. The subordinates are experts, one at detecting threats and the other at detecting neutrals. The team has access to noisy measurements from three sensors; one global, shared by all three DMs, and two local, dedicated to each of the two subordinates. The primary DM makes the final team decision based on the reports of the subordinates and the global measurement, and attaches a confidence level to the decision. A normative model for the distributed detection process is developed. The normative predictions are compared with the experimental data to identify cognitive biases of human DMs. A normative-descriptive model that accounts for these biases is developed, and is shown to provide an excellent match with the experimental data. >
['Ranga Mallubhatla', 'Krishna R. Pattipati', 'David L. Kleinman', 'Zhuang Bo Tang']
A model of distributed team information processing under ambiguity