Abstract
A significant increase in System-of-Systems (SoS) is currently observed in the social and technical domains. As a result of the increasing number of constituent system components, Systems of Systems are becoming larger and more complex. Recent research efforts have highlighted the importance of identifying innovative statistical and theoretical approaches for analyzing complex systems to better understand how they work. This paper portrays the use of an agnostic two-stage examination structure for complex systems aimed towards developing an information theory-based approach to analyze complex technical and socio-technical systems. Towards the goal of characterizing system complexity with information entropy, work was carried out in exploring the potential application of entropy to a simulated case study to illustrate its applicability and to establish the use of information theory within the broad horizon of complex systems. Although previous efforts have been made to use entropy for understanding complexity, this paper provides a basic foundation for identifying a framework to characterize complexity, in order to analyze and assess complex systems in different operational domains.
Highlights
Complex systems are referred to as systems that are composed of many independent system elements playing a key role in the whole system’s behavior
The simulated case study considered in this paper considers complex networks
To examine the use the information theoretic approach, we mapped the similarity of a complex network to a general communication channel
Summary
Complex systems are referred to as systems that are composed of many independent system elements playing a key role in the whole system’s behavior. As an attempt to contribute to the scientific body of knowledge that addresses understanding systems complexity, this paper illustrates a framework to characterize complex systems based on the concept of Information Entropy, using a two-stage examination method. The novelty of the proposed approach lies in establishing a translatable framework across complex system domains to use information entropy This includes incorporating a well-established and validated statistical foundation that shall be able to assess the relationships between sub-systems/components of a complex system, provide information to understand a system, assess and identify the interaction patterns of components/subsystems in a system, and be able to differentiate between the input and output information of a system.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
