Control charts as the preferred measurement tool for complex systems

Complex systems are a typical, messy part of life. One way to create a complex system is by the simple aggregation of systems that can result in the emergence of synergistic and/or antagonistic reactions that could possibly cause a significant change in outcome. One of the difficulties of working with complex systems is input data are not available to use for deterministic or stochastic solutions. Complex systems created by aggregation are prevalent in nature with examples being mineral exploration and environmental monitoring data. When one decomposes a complex system formed by aggregation, variation is found to be nested. This nesting is, when by design, to ensure steady growth and to hedge against declines. However, each factor cannot be quantitatively defined so the covariance cannot be calculated. Complex systems display further complication in that they display growth/decline as a function of time that challenges assumptions about the use of the classic control chart. This paper explores how to assess a non-zero-sloping complex system representing a man-made case, specifically mutual fund selection. This paper introduces the I-RASR control chart as a tool that allows the assessment of control status for non-zerosloping data, and, by extension, for complex systems in general. Copyright