Engineering Emergence

A Modeling and Simulation Approach

Larry B. Rainey, Mo Jamshidi, Larry B. Rainey, Mo Jamshidi

• Publisher: CRC Press
• Year: 2018
• eBook ISBN: 9781351694841
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

This book examines the nature of emergence in context of man-made (i.e. engineered) systems, in general, and system of systems engineering applications, specifically. It investigates emergence to interrogate or explore the domain space from a modeling and simulation perspective to facilitate understanding, detection, classification, prediction, control, and visualization of the phenomenon. Written by leading international experts, the text is the first to address emergence from an engineering perspective.

"System engineering has a long and proud tradition of establishing the integrative view of systems. The field, however, has not always embraced and assimilated well the lessons and implications from research on complex adaptive systems. As the editors' note, there have been no texts on Engineering Emergence: Principles and Applications. It is therefore especially useful to have this new, edited book that pulls together so many of the key elements, ranging from the theoretical to the practical, and tapping into advances in methods, tools, and ways to study system complexity. Drs. Rainey and Jamshidi are to be congratulated both for their vision of the book and their success in recruiting contributors with so much to say. Most notable, however, is that this is a book with engineering at its core. It uses modeling and simulation as the language in which to express principles and insights in ways that include tight thinking and rigor despite dealing with notably untidy and often surprising phenomena."

— Paul K. Davis, RAND and Frederick S. Pardee RAND Graduate School

• The first chapter is an introduction and overview to the text.
• The book provides 12 chapters that have a theoretical foundation for this subject.
• Includes 7 specific example chapters of how various modeling and simulation paradigms/techniques can be used to investigate emergence in an engineering context to facilitate understanding, detection, classification, prediction, control and visualization of emergent behavior.
• The final chapter offers lessons learned and the proposed way-ahead for this discipline.

IITheoretical Perspectives

3DEVS-Based Modeling and Simulation Framework for Emergence in System of Systems

Bernard P. Zeigler

3.1Introduction

3.2DEVS Provides the Components and Coupling for Models of Complex Systems

3.2.1Theory of M&S for System of Systems

3.2.1.1Illustrative Example: Turing Machine

3.2.1.2TM Control

3.2.2Active-Passive Compositions

3.2.3Hierarchy of System Specifications

3.2.4M&S Framework (MSF)

3.2.4.1The Entities of the Framework

3.3DEVS Supports Dynamic Structure for Genuine Adaptation and Evolution

3.4Experimental Frame Supports Emergence Behavior Observation

3.4.1Positive and Negative Emergence

3.4.1.1Negative Emergence

3.4.2Emergence of Topics in Twitter

3.4.3Emergence Monitoring and Detection

3.5DEVS Markov Models Support Prediction of Emergence

3.6DEVS Enables Fundamental Emergence Modeling

3.7Conditions for Positive Emergence and Examples of DEVS Emergence Modeling

3.7.1Positive Emergence

3.7.2Emergence in National Healthcare Systems

3.7.3Emergence of Language Capabilities in Human Evolution

3.8Conclusion and Perspective

References

3.1Introduction

Emergence as it has been recently treated has both subjective and objective aspects. Objectively, for emergence to be observed, there are changes in the system that surprise the observer. However, such changes may not be significant enough to cause a more fundamental shakeup in understanding. Mittal [1] makes the point that strong emergent behavior results in generation of new knowledge about the system in the form of one or more new abstraction levels and linguistic descriptions, new hierarchical structures and couplings, new component behaviors, and new feedback loops representing previously unperceived complex interactions. Once understood and curated, the behavior returns to the weak form, as it is no longer intriguing, and then can begin to be treated in regularized fashion. Moreover, emergent behavior is likely an inherent feature of any complex system model because abstracting a continuous real-world system (e.g., any complex natural system) to a constructed system model must leave gaps of representation that may diverge in unanticipated directions. Since abstraction is needed to limit the inherently infinite state space to a finite set of tractable and semantically labelled states, Mittal [2] argues that the model’s dynamical behavior must account for the elapsed continuous time in the interval between any pair of successive states. This is the case for any computational model whether nominally continuous or discrete. Moreover, high performance computing and big data allow more points to fill the gaps but can never cover the space completely (cf. the mathematics of rationals and reals). Philosophically, following Ashby [3] and Foo and Zeigler [4], the perceived global behavior (holism) of a model might be characterized as: components (reductionism) + interactions (computation) + higher-order effects where the latter can be considered as the source of emergent behaviors [5,6]. In this chapter, we present some features of DEVS that make it the right formalism to use to support the abstraction and observation necessary to deal with emergence in complex systems [7]. We will make the following points:

• DEVS provides the components and couplings for models of complex systems.
• DEVS supports dynamic structure for genuine adaption and evolution.
• Experimental frame supports emergence behavior observation.
• DEVS Markov models support prediction of emergence.
• DEVS enables fundamental emergence modeling.

In the rest of this chapter, we expand on these points, after which we provide some fundamental conditions that may be necessary in order to engineer positive emergence. Finally, we discuss two distinct examples where the application of the concepts and tools discussed here offer clarification and readiness for researcher adoption.

3.2DEVS Provides the Components and Coupling for Models of Complex Systems

Components and couplings in complex system models must include representation of decision making in natural and artificial environments. DEVS has the universality [8] to represent the discrete (for agent models) and continuous (for natural environments) as well as hybrid (for artificial environments) formalism types needed for adequate complex system model construction. DEVS supports dynamic structure for genuine adaption and evolution. Strong dynamic structure capabilities are needed to specify and flexibly control the changes in components and their coupling to be able to adequately model adaptation, evolution, and emergence in ways that include the possibility of genuine surprise. Recently, a next generation of dynamic structure formalisms has been under development in the DEVS community [9,10,11]. We will briefly review the concepts in the context of an overall framework for modeling and simulation (M&S) based on systems theory and capable of representing existing formulations of dynamic structure [8,12].

3.2.1Theory of M&S for System of Systems

In systems theory as formulated by Wymore [13,14], systems are defined mathematically and viewed as components to be coupled together to form a higher level system.

Wymore’s [14] systems theory mathematically characterizes:

• Systems as well-defined mathematical objects characterizing “black boxes” with structure and behavior.
• Composition of systems—constituent systems and coupling specification result in a system, called the resultant, with structure and behavior emerging from their interaction.
• Closure under coupling—the resultant is a well-defined system just like the original components.

System of Systems (SoS) is a composition of systems, where often component systems have legacy properties, e.g., autonomy, belonging, diversity, and emergence [15]. In this view, a SoS is a system with the distinction that its parts and relationships are gathered together under the forces of legacy (components bring their pre-existing constraints as extant viable systems) and emergence (it is not totally predictable what properties and behavior will emerge.) Here, in Wymore’s terms, coupling captures certain properties of relevance to coordination, e.g., connectivity, information flow, etc. Structural and behavioral properties provide the means to characterize the resulting SoS, such as fragmented, competitive, collaborative, coordinated, etc.

The Discrete Event Systems Speci...

Table of contents

• Cover
• Half Title Page
• Title Page
• Copyright Page
• Dedication
• Contents
• Foreword
• Editors
• Contributors
• Section I Introduction and Overview
• Section II Theoretical Perspectives
• Section III Theoretical Perspectives with Practical Applications
• Section IV Summary
• Index