The Dual Node Decision Wheels (DNDW) architecture concept was previously described as a novel approach toward integrating analytic and decision-making processes in joint human/automation systems in highly complex sociotechnical settings. In this paper, we extend the DNDW construct with a description of components in this framework, combining structures of the Dual Node Network (DNN) for Information Fusion and Resource Management with extensions on Rasmussen’s Decision Ladder (DL) to provide guidance on constructing information systems that better serve decision-making support requirements. The DNN takes a component-centered approach to system design, decomposing each asset in terms of data inputs and outputs according to their roles and interactions in a fusion network. However, to ensure relevancy to and organizational fitment within command and control (C2) processes, principles from cognitive systems engineering emphasize that system design must take a human-centered systems view, integrating information needs and decision making requirements to drive the architecture design and capabilities of network assets. In the current work, we present an approach for structuring and assessing DNDW systems that uses a unique hybrid DNN top-down system design with a human-centered process design, combining DNN node decomposition with artifacts from cognitive analysis (i.e., system abstraction decomposition models, decision ladders) to provide work domain and task-level insights at different levels in an example intelligence, surveillance, and reconnaissance (ISR) system setting. This DNDW structure will ensure not only that the information fusion technologies and processes are structured effectively, but that the resulting information products will align with the requirements of human decision makers and be adaptable to different work settings .
The Dual Node Decision Wheels (DNDW) architecture is a new approach to information fusion and decision support systems. By combining cognitive systems engineering organizational analysis tools, such as decision trees, with the Dual Node Network (DNN) technical architecture for information fusion, the DNDW can align relevant data and information products with an organization’s decision-making processes. In this paper, we present the Compositional Inference and Machine Learning Environment (CIMLE), a prototype framework based on the principles of the DNDW architecture. CIMLE provides a flexible environment so heterogeneous data sources, messaging frameworks, and analytic processes can interoperate to provide the specific information required for situation understanding and decision making. It was designed to support the creation of modular, distributed solutions over large monolithic systems. With CIMLE, users can repurpose individual analytics to address evolving decision-making requirements or to adapt to new mission contexts; CIMLE’s modular design simplifies integration with new host operating environments. CIMLE’s configurable system design enables model developers to build analytical systems that closely align with organizational structures and processes and support the organization’s information needs.
As the modern information environment continues to expand with new technologies, military Command and Control (C2) has increasing access to unprecedented amounts of data and analytic resources to support military decision making. However, with the increasing quantity and heterogeneity of multi-INT data—from new collection platforms, new sensors, and new analytic tools—comes a growing information fusion challenge. For example, increasingly distributed processing, exploitation, and dissemination (PED) capabilities and analyst intelligence resources must identify and integrate the most relevant data sources to support and improve operational command and control and situation awareness without becoming overwhelmed by data and potentially missing critical information. We present an innovative new information <i>fusion </i>and organizational decision-making architecture—Dual Node Decision Wheels (DNDW)—that integrates multi-INT PED, information analysis, and C2 processes through a novel combination of goal-directed information fusion and data-driven decision making, helping alleviate “big data” challenges through more fluid coordination of organizations and technologies. DNDW applies the dual node network for fusion and resource management with semantic links between organizational processes and decision aides, ensuring that each organizational role has access to the right information. DNDW can map fusion onto any organizational structure and provide a cost-effective solution methodology for integrating new technologies.