Proactive Decision Support Tools & Design Schema for Dynamic/Uncertain Environments

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OBJECTIVE: Support improved operational (e.g. combat system; command & control (C2)) decision making under high stress, uncertain operational conditions through the development of proactive, context-based decision support aids. The objective of this project is to create a scientifically-principled design specification and prototype concepts for a set of decision aids capable of supporting rapid adaptive planning and dynamic execution across evolving missions with dynamic tasking requirements. The result will be a consistent approach to proactive decision support that: 1) will facilitate rapid, affordable development for different functions in the combat center; 2) minimize training, 3) allow rapid insertion of decision support concepts into C2 & combat systems, and 4) increase end user adoption and utilization of tools developed in accordance with the design schema. DESCRIPTION: Many military domains require agile, time-critical decision making in order to achieve the speed of command required and dynamic re-planning. Further, these domains increasingly involve diverse and/or complex missions under conditions of high uncertainty. Undersea warfare, for example, is becoming more complex, with submarines tasked to execute an ever wider array of missions in dynamic, unpredictable and environmentally-challenging operational environments [1]. As the proliferation of new generations of quiet diesel and other submarine technologies continues across the globe, the US nuclear submarine force must continually improve its undersea warfare detection and tracking capabilities to preserve its superiority [2]. Improvements to sensors and new automation capabilities are constantly coming on-stream to offer support for warfighters. However, it is challenging to integrate these capabilities into the current combat systems and command and control (C2) architectures. The current combat information systems are data-centric, relatively inflexible to dynamic mission demands, and insensitive to mission context. Current Operator Machine Interfaces (OMIs) need to be task-centric and support the task needs of decision makers. These systems are poorly attuned to the requirements of dynamic new operational scenarios. Too often, decision makers find themselves hunting for, and manually integrating task-relevant mission information through the use of ad hoc tools, such as note pads, white boards, spreadsheets, PowerPoint, etc. [3,4], which are inherently fragile and devoid of the dynamic context needed to rapidly re-plan a mission in response to modifications to either top-down operational objectives or bottom-up tactical constraints. The result is often sub-optimal, reactive decision making where the combat center is constrained when reacting to unexpected or uncertain situations (i.e. changing context). What is needed are consistent and coherent supervisory control schemes for anticipatory automation including supporting decision support and associated OMIs that are: flexible, task & mission context-sensitive, and enablers for proactive decision making. The key to addressing these needs is to quickly provide shared understanding across the echelons of command for the plan and its various contingencies, how it is unfolding based on operational metrics, and proposed modifications to maintain mission objectives, i.e. ensuring there is proactively shared context. The last 30 years have seen a revolution in our understanding of human decision making, as recognized by the award of Nobel prizes for decision science [5-9]. The properties and processing styles of two systems of judgment and decision making have been identified, operationally demonstrated and widely accepted the fast, heuristic"System 1"and the slow, deliberate"System 2"[6]. The negative consequences of time-pressured decision making with System 1 decision making have been well established [5,6]. Further, in applied science, there have been advances in understanding how to effectively employ automation and how to make it simple and transparent enough to harness for improved end user decision making [10, 11]. This STTR topic seeks to advance and demonstrate the systematic application of these findings with a specific focus on Operational decision making under stress to allow more rapid re-planning and execution, which has been referred to as adaptive planning or dynamic operations.. Despite scientific advances in the application of cognitive decision making, on the one hand, and in integrating automation, on the other, there has been a singular failure to leverage and apply this work to military decision making in any principled, sustained and coherent fashion. The Office of Naval Research (ONR) is interested in applying the wealth of decision making science and transitioning it for the Navy, specifically to improve warfighter decision making. Rather than creating a system specific decision aids for a particular military application, ONR is interested in the creation of an extensible"design schema"for operational / tactical decision support systems. A consistent set of design guidelines should be established to influence the development of an array of decision aids tailored to various warfare domains, yet possessing a consistent design approach. Such consistency is particularly timely at a time of budgetary pressure to facilitate efficient development and deployment of successful proactive decision support across military applications, minimize training, and increase end user adoption and successful employment. Consistency in decision support is also required to assists staffs in merging individual warfare area plans and their status into an integrated operational plan within and across echelons. The schema should leverage and apply advances in cognitive decision making research to ensure the system meshes with, and addresses the needs of, agile decision makers appropriate to the task and context at hand. Developing the requirements for these schemas and demonstrating their utility in the context of an example undersea warfare decision making task is desirable but one-of-a-kind, non-extensible custom solutions will not be considered responsive to this topic. PHASE I: 1. Analyze the information and context representation requirements of a cognitively challenging, dynamic operationally relevant decision making tasks that may be supported by automation. 2. Identify and apply suitable cognitive decision making and supervisory control research to identify the requirements for a decision support schema at the operational (staff) and/or tactical (platform) levels. 3. Propose notional elements for an extensible decision support design schema and mechanisms by which these elements might be dynamically adapted to support mission execution and re-planning tasks. 4. Design and prototype a basic proof-of-concept decision support system that demonstrates improved speed of command required to perform re-planning. Phase I deliverables should include a Final Phase I report that includes a detailed description of the approach taken and results obtained in Tasks 1-4 as well as a detailed approach for Phase II. PHASE II: 1. Mature, demonstrate, and refine a concept prototype illustrating the decision support concept. 2. Validate the decision support concept and quantify its impact on decision making tasks with controlled human performance study with dynamically changing mission / task requirements with empirical, user-based performance data. 3. Codify the decision support concept in a schema, or template, for application to an array of an array of warfare command decisions. 4. Demonstrate that the schema can accommodate a variety of different decision making tasks. 5. Develop a transition strategy for insertion of the technology developed into a US Navy Program of Record (e.g. BYG-1, GCCS-M) or a commercially developed system. Phase II deliverables should include a Final Phase II report that includes a detailed description of the approach taken and results obtained in Tasks 1-5. PHASE III: Tailor, transition, test and deliver system to an identified DoD program of record (POR) through an appropriate advanced development funded process and/or commercial operational setting. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: The development of Proactive decision support technologies, particularly as a generic concept designed to be rapidly re-configured to address different decision making tasks will create a significant competitive advantage for architecting and deploying future decision support systems. We anticipate the proposed technologies could be a significant benefit to: air traffic control systems; emergency management systems; as well as the managing of complex, dynamic, highly autonomous systems. REFERENCES: 1. Commander, Submarine Forces. (July, 2011). Design for Undersea Warfare. Washington, DC. 2. Submarine Tactical Requirements Group. (April, 2011). Prioritized focus areas. Norfolk, VA. 3. Kirschenbaum, S.S. (2001). Submarine decision making. In E. Salas & G. Klein (Eds.), Linking expertise and naturalistic decision making. Mahwah, NJ: Lawrence Erlbaum & Associates. 4. Dominguez, C., Long, W.G., Miller, T.E., & Wiggins, S.L. (2007). Design Directions for Support of Submarine Commanding Officer Decision Making. Klein Associates Technical Report, Deerborn, OH. 5. Kahneman, D. (2003). Maps of bounded rationality: A perspective on intuitive judgment and choice. In T. Frangsmyr (Ed.), Les Prix Nobel 2002. Stockholm, Sweden. 6. Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate. Behavioral and Brain Sciences, 23, 645665. 7. Gigerenzer, G., & Brighton, B. (2009). Homo Heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 1007-143. 8. Hollnagel E (1993). Human reliability analysis: context and control. Academic Press, London 9. Feigh, K.M. (2010). Incorporating multiple patterns of activity into the design of cognitive work support systems. Cognitive Technology & Work. 10. Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model of types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 30, 286297. 11. St. John, M., Smallman, H.S., Manes, D.I., Feher, B.A., and Morrison, J.G. (2005). Heuristic automation for decluttering tactical displays. Human Factors, 47, 509-525.

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