Distributed binary detection with different local hypotheses

The authors consider a generalized distributed binary hypothesis-testing problem with a hierarchical team. In this problem, the subordinate decision-makers (DMs) transmit their opinions on their own local hypotheses, which are only probabilistically related to the global hypotheses at the primary DM. It is shown that the normative decision strategies of all DMs are coupled likelihood-ratio tests, but the decision thresholds are also a function of the joint probability distribution of hypotheses at all DMs. To assess the discrimination capabilities of a team, the authors introduce the concept of a team relative (receiver) operating characteristic curve. The concept was tested on teams of humans using a hypothetical medical diagnosis task. Potential human biases leading to discrepancies between the normative predictions and experimental results were identified. These form the basis for a normative-descriptive model currently under development