Test and Evaluation Resource Allocation Using Uncertainty Reduction

Bjorkman, Eileen A.; Sarkani, Shahram; Mazzuchi, Thomas A.

doi:10.1109/tem.2012.2227972

Cited by 8 publications

(12 citation statements)

References 25 publications

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“…Unit selection is arbitrary as long as consistency is maintained for compared results. 59 Information entropy for a continuous random variable can be calculated using…”

Section: Quantitatively Evaluating C2 Performancementioning

confidence: 99%

Evaluating the agility of adaptive command and control networks from a cyber complex adaptive systems perspective

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Domerçant

Mavris

2015

Journal of Defense Modeling & Simulation

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Command and control (C2) networks are critical components of modern military systems, enabling information sharing and communications between systems. These systems operate in complex environments characterized by uncertain and evolving threats, creating a need for agile C2 networks. This paper presents a Cyber Complex Adaptive Systems approach for assessing the agility of adaptive C2 networks. Agent-based modeling is used to simulate the performance of a C2 network connecting unmanned aerial vehicles for a collaborative surveillance mission. Due to the importance of information sharing in C2, information entropy-based awareness is used to quantitatively evaluate C2 performance. Complex network methods are used to define initial network topologies and threats. Network adaptation through random rewiring is shown to recover lost C2 capabilities following network attacks, and in some cases improve performance relative to initial topologies. Inverse average path length and largest connected component fraction are shown to be important factors for maintaining C2 awareness, with inverse average path length being the better indicator of awareness.

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“…Unit selection is arbitrary as long as consistency is maintained for compared results. 59 Information entropy for a continuous random variable can be calculated using…”

Section: Quantitatively Evaluating C2 Performancementioning

confidence: 99%

Evaluating the agility of adaptive command and control networks from a cyber complex adaptive systems perspective

Tran

Domerçant

Mavris

2015

Journal of Defense Modeling & Simulation

View full text Add to dashboard Cite

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“…Bjorkman et al [62] examined the optimization of resource allocation in the context of designing equipment tests for the US Department of Defense. This study was particularly interesting as the primary objective was to reduce the uncertainty associated with the testing process rather than strictly improving some measure of value.…”

Section: Applications Of Portfolio Optimization In the Defence Sementioning

confidence: 99%

“…It was concluded that the optimized portfolios had a value of 4.3% to 8.4% higher than the portfolios selected by SMEs, for the same cost. Furthermore, it was highlighted that the largest difference was obtained with the lowest-valued portfolio, which could indicate that the optimization process was most effective for resource-constrained environments [62].…”

Section: Applications Of Portfolio Optimization In the Defence Sementioning

confidence: 99%

“…Bjorkman et al [62] used Monte Carlo sampling to estimate the baseline uncertainty in the prediction of airplane landing roll distances under various test conditions.…”

Section: A Uncertaintymentioning

confidence: 99%

“…Bizkevelci and Çakmak [50] derived a prioritization scheme based on the needs of a particular mission and the areas of technology that were required. The study of Bjorkman et al [62] considered the entropy associated with testing measurements as a measure of value. While an interesting approach, this technique is not widely applicable to portfolio optimization.…”

Section: B Quantification Of Valuementioning

confidence: 99%

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Portfolio Optimization for Defence Applications

et al. 2020

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The problem of designing an effective future defense force is quite complex and challenging. One methodology that is often employed in this domain is portfolio optimization, whereby the objective is to select a diverse set of assets that maximize the return on investment. In the defense context, the return on investment is often measured in terms of the capabilities that the investments will provide. While the field of portfolio optimization is well established, applications in the defense sector pose unique challenges not seen in other application domains. However, the literature regarding portfolio optimization for defense applications is rather sparse. To this end, this paper provides a structured review of recent applications and identifies a number of areas that warrant further investigation.

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