Using Multiattribute Utility Copulas in Support of UAV Search and Destroy Operations

The multiattribute utility copula is an emerging form of utility function used by decision analysts to study decisions with dependent attributes. Failure to properly address attribute dependence may cause errors in selecting the optimal policy. This research examines two scenarios of interest to the modern warfighter. The first scenario employs a utility copula to determine the type, quantity, and altitude of UAVs to be sent to strike a stationary target. The second scenario employs a utility copula to examine the impact of attribute dependence on the optimal routing of UAVs in a contested operational environment when performing a search and destroy mission against a Markovian target. Routing decisions involve a tradeoff between risk of UAV exposure to the enemy and the ability to strike the target. This research informs decision makers and analysts with respect to the tactics, techniques, and procedures employed in UAV search and destroy missions. An ever increasing UAV operations tempo suggests such research becoming increasingly relevant to the warfighter

Statistical Inference to Evaluate and Compare the Performance of Correlated Multi-State Classification Systems

The current emphasis on including correlation when comparing diagnostic test performance is quite important, however, there are cases in which correlation effects may be negligible with respect to inference. This proposed work examines the impact of including correlation between classification systems with continuous features by comparing the optimal performance of two diagnostic tests with multiple outcomes as well as providing inference for a sequence of tests. We define the optimal point using Bayes Cost, a metric that sums the weighted misclassifications within a diagnostic test using a cost/benefit structure. Through simulation, we quantify the impact of correlation on standard errors comparing two tests and evaluate the resulting errors with respect to CI coverage and width under varying diagnostic test accuracy, sample size, cost/benefit structures, parametric assumptions and correlation levels. When formulas are required for better inference to include correlation, we provide updated computational techniques that properly extend the Delta and Generalized method. Additionally, to date, no methods have been applied to quantify the performance of a sequence of tests. Therefore, the inference methods derived in this work are extended to sequenced tests where feature correlation is unavoidable and must be accounted for when developing inference on tests