Complexity Based Risk Evaluation in Engineered Systems

Fischi, Jonathan; Nichiani, Roshanak

doi:10.1016/j.procs.2015.03.044

Cited by 8 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: A Measuring and Managing System Complexitymentioning

confidence: 99%

Method for Measuring Resilience of Complex Systems Using Network Theory and Graph Energy

Edwards,

Vierlboeck,

Nilchiani

et al. 2024

IEEE Open J. Syst. Eng.

View full text Add to dashboard Cite

Because of societies' dependence on systems that have increasing interconnectedness, governments and industries have increasing interest in managing the resilience of these systems and the risks associated with their disruption or failure. The identification and localization of tipping points in complex systems is essential in predicting system collapse but exceedingly difficult to estimate. At critical tipping-point thresholds, systems may transition from stable to unstable and potentially collapse. One of the approaches to measure a complex system's resilience to collapse has been to model the system as a network, reduce the network behavior to a simpler model, and measure the resulting model's stability. In particular, Gao and colleagues introduced a method in 2016 that includes a resilience index that measures precariousness, the distance to tipping points. However, those mathematical reductions can cause the model to lose information on the topological complexity of the system. Using computational experimentation, a new method has been formulated that moreaccurately predicts the resilience and location of tipping points in networked systems by integrating Gao et al.'s method with a measurement of a system's topological complexity using graph energy, which arose from molecular orbital theory. Herein, this method for measuring and managing system resilience is outlined with case studies involving ecosystem collapse, supply-chain sustainability, and disruptive technology. The precariousness of these example systems is found using the dimension reduction, and a graph-energy correction is quantified to fine-tune the measurement. Lastly, the integration of this method into systems engineering processes is explored to provide a measurement of precariousness and give insight into how a complex system's topology affects the location of its tipping points.

show abstract

Section: A Measuring and Managing System Complexitymentioning

confidence: 99%

Method for Measuring Resilience of Complex Systems Using Network Theory and Graph Energy

Edwards,

Vierlboeck,

Nilchiani

et al. 2024

IEEE Open J. Syst. Eng.

View full text Add to dashboard Cite

show abstract

“…For measuring complexity, many sources of quantitative complexity measures from literature focus on structural, dynamic, organizational, and information/computational complexity. 34,35 Because the approach of this research Models a system with a graph representation and reduces the system content using weighted sums, the most relevant complexity measures from the literature are those that use similar graph representations and reductions. Complexity metrics with these characteristics include those derived from entropy [35][36][37][38] and cyclomatic complexity.…”

Section: Measuring System Complexitymentioning

confidence: 99%

“…34,35 Because the approach of this research Models a system with a graph representation and reduces the system content using weighted sums, the most relevant complexity measures from the literature are those that use similar graph representations and reductions. Complexity metrics with these characteristics include those derived from entropy [35][36][37][38] and cyclomatic complexity. 39 Out of these reviews of complexity metrics, graph energy was selected because of: (1) previous research showing its use as a measurement of topological complexity in systems engineering, discussed below; (2) its history of providing insight into tipping-point behavior in molecular systems, as discussed in Section 1; (3) potential to be integrated with method from Gao et al due to its similar mathematical form, as shown in Section 3.…”

Section: Measuring System Complexitymentioning

confidence: 99%

Impact of graph energy on a measurement of resilience for tipping points in complex systems

Edwards,

Nilchiani,

Miller

2024

Systems Engineering

View full text Add to dashboard Cite

Societies depend on various complex and highly interconnected systems, leading to increasing interest in methods for managing the resilience of these complex systems and the risks associated with their disruption or failure. Identifying and localizing tipping points, or phase transitions, in complex systems is essential for predicting system behavior but a difficult challenge when there are many interacting elements. Systems may transition from stable to unstable at critical tipping‐point thresholds and potentially collapse. One of the suggested approaches in literature is to measure a complex system's resilience to collapse by modeling the system as a network, reducing the network behavior to a simpler model, and then measuring the resulting model's stability. In particular, Gao and colleagues introduced a methodology in 2016 that introduces a resilience index to measure precariousness (the distance to tipping points). However, those mathematical reductions can cause information loss from reducing the topological complexity of the system. Herein, the authors introduce a new methodology that more‐accurately predicts the location of tipping points in networked systems and their precariousness with respect to those tipping points by integrating two approaches: (1) a new measurement of a system's topological complexity using graph energy (created based on molecular orbital theory) and; (2) the resilience index method from Gao et al. This new approach is tested in three separate case studies involving ecosystem collapse, supply chain sustainability, and disruptive technology. Results show a shift in tipping‐point locations correlated with graph energy. The authors present an equation that corrects errors introduced as a result of the model reduction, providing a measurement of precariousness that gives insight into how a complex system's topology affects the location of its tipping points.

show abstract

“…The metric has been used to measure complexity in a multitude of fields and applications and is still applied as a measure of graph complexity [24]. Furthermore, Shannon's metric has inspired the development of various other information based complexity metrics [25], which also include metrics such as Gell-Mann's [26,27] effective complexity. Other metrics that have gained popularity over time are McCabe's Cyclomatic Complexity [12] and Halstead's approach [28], both for software code.…”

Section: Complexity and Metricsmentioning

confidence: 99%

Natural Language Processing to Assess Structure and Complexity of System Requirements

Vierlboeck¹,

Nilchiani²,

Blackburn³

2022

Preprint

View full text Add to dashboard Cite

<p>The development process of a system is shaped by many variables that influence the progress and outcome. As a result, complexity can increase throughout the development process with potential negative consequences, which makes the handling of complexity critical. Most development processes begin with the definition of needs and requirements, and in this paper, the authors present a novel approach that allows for the automated extraction of structure from requirement specifications. This approach uses Natural Language Processing to elicit three structural layers from a set of requirements and subsequently analyzes them with metrics used to assess complexity. In a case study, the approach is illustrated using a set of 79 requirements in which 246 individual entities are identified. These entities, as well as the requirements are structured and analyzed using network density and spectral entropy. The metrics allow for interpretation and insight generation, such as an increase in the number of potentially problematic loops. The approach achieved a detection and structural accuracy of over 98 percent for the given case study and is planned to be expanded with future cases.</p>

show abstract

Complexity Based Risk Evaluation in Engineered Systems

Cited by 8 publications

References 5 publications

Method for Measuring Resilience of Complex Systems Using Network Theory and Graph Energy

Method for Measuring Resilience of Complex Systems Using Network Theory and Graph Energy

Impact of graph energy on a measurement of resilience for tipping points in complex systems

Natural Language Processing to Assess Structure and Complexity of System Requirements

Contact Info

Product

Resources

About