Mutual Influences in Interwoven Systems and their detection in the context of Organic Computing

TL;DR

This paper discusses the complexity of large interwoven systems, introduces a methodology to detect mutual influences among subsystems, and provides a taxonomy to guide the selection of detection methods within Organic Computing.

Contribution

It presents a novel methodology for detecting mutual influences in complex systems and offers a taxonomy to assist in choosing appropriate detection approaches.

Findings

Methodology successfully detects mutual influences in example systems.

Real-world examples illustrate the complexity and importance of influence detection.

Taxonomy aids in selecting suitable detection methods for Organic Computing applications.

Abstract

Technical systems have evolved over time into large and complex Interwoven Systems consisting of several to a huge number of (possibly heterogeneous) subsystems that have interdependencies. The resultant mutual influences among subsystems have made them so complex that they are no longer manageable by humans and it is assumed to intensify rapidly. Identifying such mutual influences is the first step towards mastering the complexity of such systems. This paper presents mutual influences in Interwoven Systems by describing real-world examples and a methodology to detect them in the context of Organic Computing. The methodology is evaluated with the help of an example. Further, a taxonomy of Organic Computing applications helpful for selecting suitable methods for detecting hidden mutual influences is described briefly.