Complex Systems And Human Performance Modeling

Abstract

The development of a human performance model is an exercise in complexity. Despite this, techniques that are commonplace in the study of complex dynamical systems have yet to find their way into human performance modeler’s toolbox. In this paper, we describe our efforts to develop new generative and analytical methods within a task network modeling environment. Specifically, we present task network modeling techniques for generating inter-event times series typical of a complex system. We focus on communication patterns. In addition, we describe the associated analytical techniques needed to verify the time series. Again, while these analytical techniques will be familiar to the complexity scientist, they have significant and largely unrecognized methodological implications for the human performance modeler.