Human decision making and coordination strategies within a distributed organization are known to be less than optimal, especially as the workload under which the team must operate increases. This project will employ a normative-descriptive approach to systematically investigate team performance in such contexts. The fundamental tenet of the normative-descriptive theory is that motivated expert decision makers strive for optimality, but are constrained from achieving it by their inherent limitations and cognitive biases. The normative-descriptive theory employs normative solutions as a baseline, and modifies these solutions by placing psychologically interpretable constraints and structure on the team's cognitive processes to provide accurate predictions of team performance. These models, which are experimentally validated, provide a relevant basis for designing distributed database and communication subsystems that best support the needs of human decision makers. The working hypothesis put forward in this project is that human teams adapt their coordination strategies to workload demands. At low workload teams prefer to coordinate explicitly using communication channels. Under moderate workload teams rely on implicit coordination, exercising internal models to anticipate the needs of other team members. Under high workload off-line pre-planning replaces on-line coordination. This project will investigate, using the normative-descriptive approach, explicit and implicit coordination in the areas of team information processing, resource allocation, and task sequencing. The research on information coordination characterizes the amount of communication needed among team members for superior explicit coordination, and the role of feedback as a means of improving implicit coordination. The research on resource and task coordination focuses on when a leader is necessary to improve coordination, and on how coordination strategies change when different mixes of sequential, parallel, and multiple actions must be taken by the team. The research on database support addresses the questions of database modeling structures needed to support the requirements of different forms of coordination in an organization, and develops algorithms for dynamically (re)locating the data in the network for effective coordination.
