Use of inductive inference models to understand human performance in supervisory control domains

This paper proposes a methodology to understand human performance in complex supervisory control domains through use of inductive inference methods, and Brunswik's lens framework. Operators in today's highly automated control systems are confronted with vast arrays of information. In order to design effective workspaces in these domains, therefore, researchers must understand information demand and utilization from a systems perspective. That is, to understand system information requirements, one must attempt to understand the diagnosticity of information in the task domain, as well as the utility of the information by human operators. This paper outlines a framework, in the spirit of Brunswik's lens model, to represent information utility and diagnosticity. Within this framework, a computational inductive inference model is described which seeks to capture human decision policies, as well as to provide a method of comparing these policies to the information environment.