On Leadership in the Complex Adaptive Systems Engineering of Enterprise Transformation

2015 ◽  
Vol 5 (3) ◽  
pp. 192-217 ◽  
Author(s):  
Brian E. White
Keyword(s):  
Systems Engineering ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complex Adaptive ◽  
Enterprise Transformation

A Complex Adaptive Systems Engineering Methodology

Insight ◽  
2021 ◽  
Vol 24 (2) ◽  
pp. 25-31
Author(s):  
Brian E. White ◽  
Mickael Bouyaud
Keyword(s):  
Systems Engineering ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complex Adaptive

OMACS

2009 ◽  
pp. 76-104 ◽  
Author(s):  
Scott A. DeLoach
Keyword(s):  
Systems Engineering ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Multi Agent Systems ◽  
Organization Model ◽  
Policy Specification ◽  
Complex Adaptive ◽  
The Face ◽  
Multi Agent ◽  
Computational Systems

This chapter introduces a suite of technologies for building complex, adaptive systems. It is based in the multi-agent systems paradigm and uses the Organization Model for Adaptive Computational Systems (OMACS). OMACS defines the knowledge needed about a system’s structure and capabilities to allow it to reorganize at runtime in the face of a changing environment and its agent’s capabilities. However, the OMACS model is only useful if it is supported by a set of methodologies, techniques, and architectures that allow it to be implemented effectively on a wide variety of systems. To this end, this chapter presents a suite of technologies including (1) the Organization-based Multiagent Systems Engineering (O-MaSE) methodology, (2) a set of policy specification techniques that allow an OMACS system to remain flexible while still providing guidance, and (3) a set of architectures and algorithms used to implement OMACSbased systems. The chapter also includes the presentation of a small OMACS-based system.

Complex adaptive systems engineering and risk reduction

2013 ◽  
Vol 166 (5) ◽  
pp. 293-300 ◽  
Author(s):  
Theresa J. Brown ◽  
Stephen H. Conrad ◽  
Walter E. Beyeler ◽  
Robert J. Glass
Keyword(s):  
Risk Reduction ◽  
Systems Engineering ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Complex Adaptive

scholarly journals MBSE with/out Simulation: State of the Art and Way Forward

Systems ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 40 ◽  
Author(s):  
Bernard Zeigler ◽  
Saurabh Mittal ◽  
Mamadou Traore
Keyword(s):  
Complex Systems ◽  
Systems Engineering ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Engineering Application ◽  
Systems Of Systems ◽  
Integrated Simulation ◽  
Model Based ◽  
Complex Adaptive ◽  
Emergency Ambulance

The limitations of model-based support for engineering complex systems include limited capability to develop multifaceted models as well as their analysis with robust reliable simulation engines. Lack of such Modeling and Simulation (M&S) infrastructure leads to knowledge gaps in engineering such complex systems and these gaps appear as epistemological emergent behaviors. In response, an initiative is underway to bring Model-Based Systems Engineering (MBSE) closer together with model-based simulation developments. M&S represents a core capability and is needed to address today’s complex, adaptive, systems of systems engineering challenges. This paper considers the problems raised by MBSE taken as a modeling activity without the support of full strength integrated simulation capability and the potential for, and possible forms of, closer integration between the two streams. An example of a system engineering application, an unmanned vehicle fleet providing emergency ambulance service, is examined as an application of the kind of multifaceted M&S methodology required to effectively deal with such systems.

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