Controlling Design Complexity with the Monterey Phoenix Approach

As system designs grow ever more complex, our ability to assimilate, process, and then make equally complex decisions is challenged to keep pace. Intricate relationships within each system, among interoperating systems, and between each system and the external elements of its environment are themselves challenged by the sheer number of moving pieces. The actual number of permutations of configurations and possible behaviors for our systems now far exceeds that which a human is capable of predicting without automated assistance. This paper demonstrates how the Monterey Phoenix (MP) approach can be used to decompose a complex problem into smaller, more manageable models. When taken separately (using human cognition), these models are easier to read and write, and when taken together (using automation), they increase awareness of the possible behaviors that are latent in a design, so that many more use cases can be exposed. Additionally, this paper utilizes a commercial flight scenario to provide an example of how a manually crafted, moderately complex activity model can be restructured into smaller, separate models that are simpler to work with, and that expose additional behavior in simulation, which is not present in the original activity model.