Transdisciplinary Systems Engineering

The twenty-first century will continue to be defined by disruptive innovation, hyper-connectivity, and increasing scale and complexity of systems. The twenty-first century will also be an era in which autonomous systems and system-of-systems (SoS) will become increasingly prevalent. These advances will surface new concerns rooted in cybersecurity, ethics, and law. These new concerns will surface new trade-offs and challenges for society to address. Collectively, addressing these concerns will require new types of thinking that emphasize trade-offs, flexibility, adaptability, and cyber-resilience, rather than point optimizations. Against this backdrop, this chapter presents ten perspectives on how we need to think to survive and thrive in a complex, hyperconnected world.

A major consequence of the world becoming increasingly hyper-connected is that problems are becoming much too complex to solve using methods from a single discipline. This recognition in part is motivating the pursuit of convergence research. Convergence is an approach to problem solving that cuts across disciplinary boundaries. It often surfaces opportunities to achieve disruptive advances that can benefit society and human quality of life. A 2014 National Academies report [1] defines convergence as “an approach to problem solving that cuts across disciplinary boundaries.” The report states that “convergence integrates knowledge, tools, and ways of thinking from life and health sciences, physical, mathematical, and computational sciences, engineering disciplines, and beyond to form a comprehensive synthetic framework for tackling scientific and societal challenges that exist at the interfaces of multiple fields.” This chapter begins with a typology of convergence followed by the promise of disciplinary convergence. It discusses different types of disciplinary convergence and the impact of convergence on system modeling. It then defines transdisciplinary systems engineering and how it benefits from disciplinary convergence.

As systems continue to grow in scope, scale, and complexity, the ability to model, analyze, and design them has become a critical systems engineering challenge. Over the past decade and a half, several model-based approaches (e.g., SysML, OPM) have been developed and employed for modeling and analyzing complex systems. These methods require familiarity with specialized engineering notation on the part of stakeholders. Unfamiliar with these modeling notations, nontechnical stakeholders are unable to contribute to upfront engineering increasing the risk of extraneous design iterations and rework that invariably lead to schedule delays and cost over-runs. Today, there is an even bigger challenge given that systems need to adapt to changing operational environments and new regulations, while having the requisite flexibility to seamlessly and opportunistically integrate emerging, new, and high-payoff technologies. In this chapter, I showed that by transforming system models into system stories all stakeholders can be meaningfully engaged. Specifically, the different stakeholders can interactively execute their own stories in virtual worlds and thereby increase their understanding and contribution to upfront engineering. I called this model-based interactive storytelling (MBIS).

MBIS begins with authoring partially scripted stories about complex systems. Stakeholders can then interact with those partially scripted stories to explore complex system behaviors and change propagation paths. In the process, they can uncover system hotspots and unintended consequences. The central idea behind MBIS is to allow stakeholders to “experience” the behavior of complex systems using stakeholder-specific lenses, and with facilities to rewind and replay stories with specific “injects.” During replay, they can pause and resume at key points, and conditionally branch and loop to explore system behavior further. These capabilities serve to not only engage stakeholders but allow them to create a mental picture of system behavior in different contexts. MBIS impacts complex systems engineering in a number of ways. These include uncovering hidden interactions and dependencies within the system, ensuring that all stakeholders contribute to collaborative design especially in upfront systems engineering, and exploring alternate futures with different technologies, assumptions, initial conditions, and CONOPS.

Option generation, and especially the generation of novel options, is a critical capability that is needed during upfront engineering and especially during system architecting. Systems architecting is an integrative, decision-rich activity that requires the generation and evaluation of novel options (i.e., alternatives) to exploit technological advances and assure compliance with changes in programmatic and institutional constraints. From a decision making perspective, option generation circumscribes the set of potential decisions/actions available to the decision maker. It makes the decision maker (e.g., system architect or designer) define the decision parameters of the problem in precise terms. It sets upper and lower bounds on the quality of the decision maker’s choices. Surprisingly, option generation is not given the attention it deserves, because many decision-theoretic models assume that the decision maker already has a predefined set of options. With this mindset, the problem becomes one of representing available options in a formal decision support framework [2, 3]. Even when the assumption of having a predefined set of options at the start was removed in subsequent methods [4], option generation remains a difficult task because it requires substantial knowledge of the problem domain (e.g., air traffic control, healthcare) and understanding of the decision context. Reducing the effort required to extract, process, and represent knowledge in usable form is a key challenge in option generation today. This chapter presents a framework for enhancing novel generation, along with principles for stimulating novel option generation. It describes the architecture and capabilities of an option generation aid along with metrics to evaluate the quality of options that are generated. It concludes by stressing the importance of novel option generation in the interconnected, hyper-competitive world of today.

Performance aiding and training have been viewed historically as distinct approaches to human performance enhancement. But should they be treated as distinct? In this chapter, I argue that these approaches are complementary and, in fact, can be viewed as lying on a continuum with aiding at one end and training at the other end. Realizing this vision has become possible with the advent of shareable content objects and the Sharable Content Object Reference Model (SCORM) framework, which essentially enables making content portable and potentially “repurposable” [1]. In this sense, the SCORM framework/standard is a key enabler of convergence between performance aiding and training. This chapter presents the methodology for integrating aiding and training using Sharable Content Objects (SCOs). With the introduction of the concept of repurposable content, human performance aiding and training can be viewed as the two ends of a human performance enhancement continuum. This chapter presents the history of performance aiding versus training along with aiding-training trade-offs. It then presents the parameterization of the aiding-training continuum, followed by the system concept and architecture for an integrated aiding-training system. It discusses content authoring and presents the criteria for evaluating the aiding-training system. It concludes with the reminder that this capability embodies the convergence of decision science, artificial intelligence, and training psychology.

The term “elegance” is typically associated with aesthetics. In complex systems design, elegance is what separates the merely functional from the truly engaging. An elegant design is often inspired by a theme that drives its creation. It engages users and encourages exploration. The process of elegant system design is a creative process that focuses on the total experience and exploits systems thinking, probing and questioning, and the use of appropriate analogies and metaphors to simplify system architecture and design by eliminating extraneous constraints that can show up with poor problem framing. This chapter presents the characteristics of elegant design and the attributes of elegant system designers. It stresses the importance of smart questions, metaphors, and analogies in creating elegant designs. It presents a heuristics-enabled elegant design approach along with quantitative and qualitative metrics to users' elegance. It concludes with a call to pursue research in this relatively nascent, high-payoff design thrust.

Cost-effective protection of complex systems and infrastructures from failures and disruptive has been a systems engineering design goal and a national imperative for well over a decade [1, 2]. Broadly speaking, this capability is called resilience. Resilience means different things in different domains (e.g., military, space, healthcare, energy). For example, in the military domain, resilience is defined as the ability of a system to adapt affordably and perform effectively across a wide range of operational contexts, where context is defined by mission, environment, threat, and force disposition [1]. In the healthcare domain, resilience depends on the magnitude and duration of the disruption (e.g., surge in patients). A short-term surge can be handled by people working overtime. A long-term surge is viewed as a trend and requires a more permanent response such as increase in personnel and capacity of facilities. A key issue in engineering resilient systems is the lengthy and costly upfront engineering process. As important, current approaches to resilient system design rely on ad hoc methods (e.g., safety nets) and piecemeal solutions when developing mechanisms to respond to disruptions and unanticipated system behaviors [3, 4]. In such approaches, observed high-level behaviors are compared to expected high-level behaviors. When the difference exceeds a certain threshold, the observed behavior is considered a problem, or a precursor to a problem. Such behaviors trigger a transition to a known safe state until the underlying problem is diagnosed and resolved. During the problem resolution, the system remains unusable. Furthermore, existing methods do not take into account the different states and modes of complex systems, nor do they address unprecedented disruptions that can occur at arbitrary times during complex system operation. They also do not address the time-dependent nature of disruptions and their impact on complex systems. In light of the foregoing, there is a pressing need for an overarching methodology for developing resilient systems, one that is preferably rooted in formal modeling approaches. The ideal formal modeling approach is one that has sufficient flexibility in its formalisms to accommodate uncertainty in system states that arise from partial observability of system behavior and unexpected disruptions. The models should also lend themselves to formal verification, and testing. And finally, the models should be able to learn from observations. To advance beyond the state of the art in model-based approaches, we need the ability to determine desired appropriate behaviors of complex systems. This is a challenge because a complex system invariably has a large state space, with some “hidden” states arising from complex interactions between the system elements and between the system and the environment. Exacerbating the problem is the fact that the state of the system is often not known because of partial observability of the system and environmental uncertainties. Additional complicating factors include incomplete understanding of system dependencies and environmental influences, likelihood of conflicts between local and global responses, and an increase in the number and types of human roles.

The word "autonomous" means having the ability for self-governance and independent operation. Autonomous vehicles (AVs) are systems that are required to exhibit requisite performance for extended durations with the desired level of reliability and safety under significant uncertainties in the environment, while compensating for system failures without external intervention. AVs today are network-enabled. Soon they will be able to communicate with other vehicles and structures in the immediate vicinity (for collision avoidance) and distant vehicles and structures (for congestion management). Most AV concepts employ a network connection to the cloud, other vehicles, and built-up structures. Most vehicle concepts today do not make autonomous decisions with respect to destination and route selection. This chapter briefly reviews AV trends and presents a system-of-systems (SoS) perspective for connected AVs. It then discusses the high-reliability imperative for AVs and presents a model-based approach for AV SoS engineering. It stresses the importance of formal modeling of AV-SoS in light of the need to formally verify system models and test system (model) behavior while assuring requisite flexibility to respond to failures and disruptions. It concludes with a discussion of lingering misconceptions, the issue of liability management, and outlook for the future.