Distributed detection in teams with partial information: a normative-descriptive model

A hierarchical team faced with a binary detection problem, wherein decision makers have access to different subsets of noise-corrupted information about the true state of the environment, is considered and a normative model is developed that aggregates the individual expertise of DMs at different levels of the hierarchy.

Abstract

A hierarchical team faced with a binary detection problem, wherein decision makers (DMs) have access to different subsets of noise-corrupted information about the true state of the environment, is considered. A normative model is developed that aggregates the individual expertise of DMs at different levels of the hierarchy. The resulting team expertise is characterized in the form of a team receiver operating characteristic (ROC) curve, thereby replacing the team by an equivalent single decision-making node. The normative model is tested against human teams in a laboratory experiment. The team objective is to minimize the cost of errors in the final decision at the primary DM, where the cost structure and the information structure are treated as independent variables. Discrepancies between normative predictions and experimental results are attributed to inherent limitations and cognitive biases of humans.>