System of Systems Stakeholder Planning in a Multi-Stakeholder, Multi-Objective, and Uncertain Environment

The United States defense planning process is currently conducted in a partially consolidated manner driven by the Joint Capabilities Integration and Development System (JCIDS) process. Decisions to invest in technology, develop systems, and acquire assets are made by individual services with coordination at the higher joint level. These individual service’s decisions are made in an environment where resource allocation and need are influenced by external stakeholders (e.g. shared system development costs, additional levied requirements, and complementary system development). The future outcome of any given decision is subject to a high degree of uncertainty stemming from both the stakeholder execution of a decision and the environment in which that execution will take place. Uncertainty in execution stems from TRL advancement, development timelines, acquisition timelines, and final deployed performance. Environmental uncertainty factors include future stakeholder resource availability, the future threat environment, cooperative stakeholder decisions, and mirrored adversary decisions. The defense planning problem can be described as an acknowledged System of Systems (SoS) planning problem. Today, methodologies exist that individually address SoS Engineering processes, the evaluation of SoS performance, and SoS system deterministic evolution. However, few approaches holistically address the SoS planning and evolution problem at the level needed to assist individual defense stakeholders in strategic planning. Current approaches do not address the impact of multiple-stakeholder decisions, multiple goals for each stakeholder, the uncertainty of decision outcomes, and the temporal component to strategic decision making. This thesis develops and tests a methodology to address defense stakeholder planning in a multi-stakeholder, multi-objective, and uncertain environment. First, a decision space is populated and captured via sampling a game framework that represents multiple stakeholder decisions as well as decision outcomes over time. A compressed Markov Decision Process (MDP) based meta-model is constructed using state-space consolidation techniques. The meta-model is evaluated using a risk-based policy development algorithm derived from combining traditional Reinforcement Learning (RL) techniques with mean-variance portfolio theory. Policy sensitivity to stakeholder risk-tolerance levels is used to develop state-based risk-tolerance sensitivity profiles and identify Pareto efficient actions. The risk-tolerance sensitivity profiles are used to evaluate both state spaces and decision spaces to provide stakeholders with risk-based insights, or rule sets, to support immediate decision making and risk-based stakeholder playbook development. The capability of the risk-based policy algorithm is tested using both elementary and complex scenarios. It is demonstrated that the algorithm can be used to extract Pareto efficient decisions as a function of risk-tolerance. The state space compression is tested via the comparison of the loss of information between the risk-based policy solutions for uncompressed and compressed state space. The full methodology is then demonstrated using a full-complexity scenario based on the joint development by France, Germany, and Spain of the SoS based Future Combat Air System (FCAS). The full complexity scenario is used to baseline the risk-based methodology against current optimal policy solution techniques. A significant increase in resulting derived insights relative to optimal policy solutions in a high uncertainty scenario is demonstrated.Ph.D

Volume 45: Full Issue

Humboldt Journal of Social Relations 50th Anniversary Edition: Becoming a Polytechni

Research papers and publications (1981-1987): Workload research program

An annotated bibliography of the research reports written by participants in NASA's Workload Research Program since 1981 is presented, representing the results of theoretical and applied research conducted at Ames Research Center and at universities and industrial laboratories funded by the program. The major program elements included: 1) developing an understanding of the workload concept; 2) providing valid, reliable, and practical measures of workload; and 3) creating a computer model to predict workload. The goal is to provide workload-related design principles, measures, guidelines, and computational models. The research results are transferred to user groups by establishing close ties with manufacturers, civil and military operators of aerospace systems, and regulatory agencies; publishing scientific articles; participating in and sponsoring workshops and symposia; providing information, guidelines, and computer models; and contributing to the formulation of standards. In addition, the methods and theories developed have been applied to specific operational and design problems at the request of a number of industry and government agencies