Exploring and assessing complex systems' behavior through model-driven storytelling

Abstract

Complex systems are difficult to analyze because of unknown interactions and dependencies among system components, and between the system and the environment. These interactions and dependencies tend to produce unpredictable behaviors that can often lead to detrimental outcomes. Traditional systems engineering that relies on reductive approaches are not suitable for the analysis and design of complex systems. This recognition has led to the development of new paradigms, methods, and tools that better enable: exploration of a complex system's behavior and tradespace; detection, diagnosis, and visualization of component/subsystem dependencies and interactions; and identification, alerting, and circumvention of potentially undesirable interactions. This paper presents a system model-driven interactive storytelling approach for analyzing interactions and dependencies within complex systems, and between complex systems and the environment. An exemplar complex system is used to convey key elements of the approach. The methodology is applicable for a variety of complex systems such as global supply chains, power and energy management, transportation networks, aerospace and aviation enterprises, and defense systems.

Economic Analysis of Model-Based Systems Engineering

“…More recently, MBSE is being augmented with interactive storytelling approaches to maximize stakeholder participation in upfront engineering [18][19][20]. This added capability will have costs associated with it as well as benefits.…”

“…This added capability will have costs associated with it as well as benefits. The costs will be associated with mapping system models into system stories that are interactively executed in virtual worlds or simulations [19,20]. The gains are elimination of rework and extraneous design iterations that typically occur when not all stakeholders have been able to contribute to upfront engineering [18].…”

Leveraging Digital Twin Technology in Model-Based Systems Engineering

“…A digital twin is first created when the physical system can start providing data to the virtual system model to create a model instance that reflects the structural, performance, maintenance, and operational health characteristics of the physical system [11,12]. The physical system can be said to be "fit for purpose" if the digital twin's behavior is analyzed and can be appropriately adjusted for a variety of contingency situations.…”

“…The MBSE tool suite in Figure 1 includes system modeling methods (e.g., SysML models, Design Structure Matrix, process dependency structure matrix, probabilistic models such as Partially-Observable Markov Decision Process (POMDP), discrete event simulation [10,11], agent-based simulation, model-based storytelling [12,13], an MBSE knowledge base (that constitutes the authoritative sources of truth), and systems engineering life cycle process models. The virtual system model can range from lightweight models to full-up models.…”

Model‐based systems engineering: Motivation, current status, and research opportunities

“…MBSE needs to add visualization and experiential viewpoints, so that nonexpert stakeholders can understand the models and provide timely and meaningful feedback. In this regard, there is ongoing research in interactive storytelling‐enabled experiential perspective and experiential design languages . This body of research is concerned with introducing an experiential perspective in MBSE.…”