Identifying decision patterns using Monterey Phoenix

An automated means to identify behavior patterns would enable system developers to gain the benefit of past results in current and future system design. Analysis of decision patterns captures the interactions of human operators, systems, and their interface with the environment. Monterey Phoenix (MP) uses a formal language thereby exploiting the precision of lightweight formal methods as a means to model behaviors across domains, with applicability to the design of systems of systems, operational procedures, communication networks, business processes, and social interaction. This paper illustrates a concept of identifying patterns in a model of a decision process for a surgical procedure and an aviation emergency serving as case analyses. The results support a Bayesian belief network and an analysis of the decision structure. Finding embedded behavior patterns may be used as a means to catalog and classify intrinsic structures or architectures that are useful to system of system developers.