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In September 2009 the U.S. Government Accountability Office (GAO) reported, “Defense Acquisitions: Many Analyses of Alternatives Have Not Provided a Robust Assessment of Weapon System Options” [U.S. Government Accountability Office, Pub. No. GAO‐09‐665, 2009, p. 1]. In their focused review of 32 Acquisition Category I programs, it was found that 10 did not conduct an Analysis of Alternatives (AoA), but rather focused on an already selected weapon system solution. Prior to Milestone A, the Department of Defense (DoD) requires that service sponsors conduct an Analysis of Alternatives (AoA). The AoA is an analytical comparison of multiple alternatives to be completed prior to committing and investing costly resources to one project or decision. Typically, however, sponsors will circumvent the process in an effort to save money or schedule, and capability requirements are proposed that are so specific that they effectively eliminate all but the preferred concepts, practically ignoring other alternatives. Decision making is one of the most challenging parts of Systems Engineering. How one feeds the decision making process is key to eliminating long term waste. “About three‐quarters of a program”s total life cycle cost is influenced by decisions made before it is approved to start development“[U.S. Government Accountability Office, Pub. No. GAO‐09‐665, 2009, pp. “2]. This study evaluates the positive benefits of defining the problem domain prior to expeditiously turning to the solution domain. The goal in any decision making process is to provide the decision maker with the ability to look into the future, and to make the best possible decision based on past and present information and future predictions. There is a need for approaches that combine available quantitative data with the more subjective knowledge of experts. Decision theory techniques have been successfully used for contrasting expert judgments and making educated choices for many years. For a successful analysis, one must focus on selection of specific criteria that are key performance drivers that can lead to an informed selection of the enabling technology. Understanding the requirements of the end state goal is key to a successful analysis and should also assist in the selection of key performance parameters. A case study example is presented to demonstrate a third tier AoA identifying an enabling technology using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) while successfully accounting for tacit knowledge of expert practitioners. © 2012 Wiley Periodicals, Inc. Syst Eng 16

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We present an analytic framework for modeling and measuring uncertainty for the scenario of unmanned aerial vehicles (UAVs) cooperatively searching for a moving target. Uncertainty exists in a UAVsassessment of teammate locations, target locations, and sensor results. As is frequently done, our framework employs probabilistic maps to represent uncertain information regarding the UAVs environment. We present new methods to update the probabilistic maps when information arrives from onboard sensors or teammate UAVs. When new information is missing or delayed, we propose a novel and straightforward diffusion approach to update probabilistic maps. The UAVs make navigation decisions based on response to potential fields generated by the probabilistic maps. Since map data have uncertainty, this leads to decision-making in uncertainty. We conclude by describing how uncertainty in the environment translates into a unique measure, velocity vector dispersion (D V ), which describes the uncertainty in the UAVs navigation decision. Thresholds related to D V may be useful to guide real-time decision policies. We present simulation results that show how the use of diffusion affects the time to locate targets. We also describe how D V varies during UAV flight and comment on its utility.