The combination of today's national security environment and mandated acquisition policies makes it necessary for military systems to interoperate with each other to greater degrees. This growing interdependency results in complex Systems-of-Systems (SoS) that only continue to grow in complexity to meet evolving capability needs. Thus, timely and affordable acquisition becomes more difficult, especially in the face of mounting budgetary pressures. To counter this, architecting principles must be applied to SoS design.;The research objective is to develop an Architecture Real Options Complexity-Based Valuation Methodology (ARC-VM) suitable for acquisition-level decision making, where there is a stated desire for more informed tradeoffs between cost, schedule, and performance during the early phases of design. First, a framework is introduced to measure architecture complexity as it directly relates to military SoS. Development of the framework draws upon a diverse set of disciplines, including Complexity Science, software architecting, measurement theory, and utility theory. Next, a Real Options based valuation strategy is developed using techniques established for financial stock options that have recently been adapted for use in business and engineering decisions. The derived complexity measure provides architects with an objective measure of complexity that focuses on relevant complex system attributes. These attributes are related to the organization and distribution of SoS functionality and the sharing and processing of resources. The use of Real Options provides the necessary conceptual and visual framework to quantifiably and traceably combine measured architecture complexity, time-valued performance levels, as well as programmatic risks and uncertainties.;An example suppression of enemy air defenses (SEAD) capability demonstrates the development and usefulness of the resulting architecture complexity & Real Options based valuation methodology. Different portfolios of candidate system types are used to generate an array of architecture alternatives that are then evaluated using an engagement model. This performance data is combined with both measured architecture complexity and programmatic data to assign an acquisition value to each alternative. This proves useful when selecting alternatives most likely to meet current and future capability needs.
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