Evolving Complex Systems Research Articles

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3.3.1 A3 Architecture Overviews: Focusing architectural knowledge to support evolution of complex systems*

AbstractCreating complex systems from scratch is time consuming and costly, therefore a good development strategy often chosen by companies is to evolve existing systems. The understanding that a company has about the impact change has on the system determines its ability to cope with system evolution. Reuse of knowledge and experience becomes therefore, essential. Complex systems are usually the result of a multidisciplinary team, which means that an effective way to capture, organize and present this knowledge, in a fashion that can be used by different disciplines and departments is crucial. Typically, some of this knowledge is present in the form of text documents. However, much of that knowledge is usually lost or hidden, especially in long‐lived systems. This leads to unexpected problems that could be prevented if the company had reused the knowledge it already has.In this paper system evolution barriers are discussed, and a method to cope with them is provided. Some companies such as Toyota have already identified the advantages of using an A3 approach2 to capture and share knowledge. We share this idea and take it several steps further. A method, based on the creation of ‘A3 Architecture Overviews’ is proposed to capture and share architectural knowledge. The method is the result of the work carried at Philips Healthcare MRI Group. We show that the proposed method to capture architectural knowledge provides an effective framework to support decision making when evolving complex systems.

On the development of agent-based models for infrastructure evolution

Infrastructure systems for energy, water, transport, information, etc., are large-scale sociotechnical systems that are critical for achieving a sustainable world. They were not created at the current global scale at once but have slowly evolved from simple local systems through many social and technical decisions. If we are to understand them and manage them sustainably, we need to capture their full diversity and adaptivity in models that respect Ashby's law of requisite variety. Models of evolving complex systems must themselves be evolving complex systems that cannot be created from scratch but must be grown from simple to complex. This paper presents a sociotechnical evolutionary modelling process for creating evolving and complex Agent-Based Models (ABMs) for understanding the evolution of large-scale sociotechnical systems such as infrastructures. It involves the continuous coevolution and improvement of a social process for model specification, the technical design of a modular simulation engine, the encoding of formalised knowledge and the collection of relevant facts. In the paper, we introduce the process design, the requirements for guiding the evolution of the modelling process and illustrate the process for ABM development by showing a series of ever more complex models.

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The evolution of the topology of high-voltage electricity networks

The electricity network represents an example of an evolving complex system. The first local networks contained only a few nodes, but within several decades, they have evolved into a highly connected continental system. The growth of these networks was influenced by various factors such as economic, demographic, political and technological developments. In this paper, we analyse the growth of the French 400 kV electricity transmission network from its establishment in 1960 until the year 2000. We study the different topological characteristics that describe the intensity of the growth process, such as the number of nodes, the number of lines, the average node connectivity and the overall length of wires. We compare these results with several economic and demographic indicators in order to identify the factors which correlate with the growth rate of the electricity network. Apart from this, we evaluate how the topological efficiency and vulnerability measures (clustering coefficient, information centrality, betweenness centrality) evolve in the course of time. The decisions regarding the power grid topology are influenced by many, very often contradictory factors, such as costs, the size of the covered area, demand, fault tolerance, reliability and quality of service. Our results yield a deeper insight into the process of Critical Infrastructure (CI) construction.

Economic dynamics with financial fragility and mean-field interaction: A model

Following Aoki’s statistical mechanics methodology [Masanao Aoki, New Approaches to Macroeconomic Modeling, Cambridge University Press, 1996; Masanao Aoki, Modeling Aggregate Behaviour and Fluctuations in Economics, Cambridge University Press, 2002; Masanao Aoki, and Hiroshi Yoshikawa, Reconstructing Macroeconomics, Cambridge University Press, 2006], we provide some insights into the well-known works of [Bruce Greenwald, Joseph Stiglitz, Macroeconomic models with equity and credit rationing, in: R. Hubbard (Ed.), Information, Capital Markets and Investment, Chicago University Press, Chicago, 1990; Bruce Greenwald, Joseph Stiglitz, Financial markets imperfections and business cycles, Quarterly journal of Economics (1993)]. Specifically, we reach analytically a closed form solution of their models overcoming the aggregation problem. The key idea is to represent the economy as an evolving complex system, composed by heterogeneous interacting agents, that can be partitioned into a space of macroscopic states. This meso level of aggregation permits to adopt mean-field interaction modeling and master equation techniques.

Symptoms of Complexity in a Tourism System

Tourism destinations behave as dynamic evolving complex systems, encompassing numerous factors and activities that are interdependent and whose relationships might be highly nonlinear. Traditional research in this field has looked after a linear approach: variables and relationships are monitored in order to forecast future outcomes with simplified models and to derive implications for management organizations. The limitations of this approach have become apparent in many cases, and several authors claim for a new and different attitude. While complex systems ideas are among the most promising interdisciplinary research themes emerged in the last few decades, very little has been done so far in the field of tourism. This article presents a brief overview of the complexity framework as a means to understand structures, characteristics, and relationships, and explores the implications and contributions of the complexity literature on tourism systems. The objective is to allow the reader to gain a deeper appreciation of this point of view.

The Economy as an Evolving Complex System III: Current Perspectives and Future Directions. Edited by LAWRENCE E. BLUME and STEVEN N. DURLAUF

The Economy as an Evolving Complex System III: Current Perspectives and Future Directions. Edited by LAWRENCE E. BLUME and STEVEN N. DURLAUF

Where is evolutionary computation going? A temporal analysis of the EC community

Studying an evolving complex system and drawing some conclusions from it is an integral part of nature-inspired computing; being a part of that complex system, some insight can also be gained from our knowledge of it. In this paper we study the evolution of the evolutionary computation co-authorship network using social network analysis tools, with the aim of extracting some conclusions on its mechanisms. In order to do this, we first examine the evolution of macroscopic properties of the EC co-authorship graph, and then we look at its community structure and its corresponding change along time. The EC network is shown to be in a strongly expansive phase, exhibiting distinctive growth patterns, both at the macroscopic and the mesoscopic level.

1.5.4 Time‐Expanded Decision Networks: A Framework for Designing Evolvable Complex Systems

AbstractThis paper describes the concept of Time‐Expanded Decision Networks (TDN), a new methodology to design and analyze flexibility in large‐scale complex systems. This includes a preliminary application of the methodology to the design of Heavy Lift Launch Vehicles for NASA's space exploration initiative. Synthesizing concepts from Decision Theory, Real Options Analysis, Network Optimization, and Scenario Planning, TDN provides a holistic framework to quantify the value of system flexibility, analyze development and operational paths, and identify designs which can allow managers and systems engineers to react more easily to exogenous uncertainty. TDN consists of five principle steps, which can be implemented as a software tool: 1. Design a set of potential system configurations 2. Quantify switching costs to create a “static network” that captures the difficulty of switching among these configurations 3. Create a time‐expanded decision network by expanding the static network in time, including chance and decision nodes 4. Evaluate minimum cost paths through the network under plausible operating scenarios 5. Modify the set of initial design configurations to exploit high‐leverage switches and repeat the process to convergence. Results can inform decisions about how and where to embed flexibility in order to enable system evolution along various development and operational paths.

Time-expanded decision networks: A framework for designing evolvable complex systems

This paper describes the concept of Time-Expanded Decision Networks (TDNs), a new methodology to design and analyze flexibility in large-scale complex systems. This includes a preliminary applicati...

Time‐expanded decision networks: A framework for designing evolvable complex systems

AbstractThis paper describes the concept of Time‐Expanded Decision Networks (TDNs), a new methodology to design and analyze flexibility in large‐scale complex systems. This includes a preliminary application of the methodology to the design of Heavy Lift Launch Vehicles for NASA's space exploration initiative. Synthesizing concepts from Decision Theory, Real Options Analysis, Network Optimization, and Scenario Planning, TDN provides a holistic framework to quantify the value of system flexibility, analyze development and operational paths, and identify designs that can allow managers and systems engineers to react more easily to exogenous uncertainty. TDN consists of five principle steps, which can be implemented as a software tool: (1) Design a set of potential system configurations; (2) quantify switching costs to create a “static network” that captures the difficulty of switching among these configurations; (3) create a time‐expanded decision network by expanding the static network in time, including chance and decision nodes; (4) evaluate minimum cost paths through the network under plausible operating scenarios; and (5) modify the set of initial design configurations to exploit high‐leverage switches and repeat the process to convergence. Results can inform decisions about how and where to embed flexibility in order to enable system evolution along various development and operational paths. © 2007 Wiley Periodicals, Inc. Syst Eng 10: 167–186, 2007

Managing complex adaptive systems — A co-evolutionary perspective on natural resource management

The overexploitation of natural resources and the increasing number of social conflicts following from their unsustainable use point to a wide gap between the objectives of sustainability and current resource management practices. One of the reasons for the difficulties to close this gap is that for evolving complex systems like natural and socio-economic systems, sustainability cannot be a static objective. Instead sustainable development is an open evolutionary process of improving the management of social–ecological systems, through better understanding and knowledge. Therefore, natural resource management systems need to be able to deal with different temporal, spatial and social scales, nested hierarchies, irreducible uncertainty, multidimensional interactions and emergent properties. The co-evolutionary perspective outlined in this paper serves as heuristic device to map the interactions settled in the networks between the resource base, social institutions and the behaviour of individual actors. For this purpose we draw on ideas from complex adaptive systems theory, evolutionary theory and evolutionary economics. Finally, we outline a research agenda for a co-evolutionary approach for natural resource management systems.

Topology of resultant networks shaped by evolutionary pressure.

Understanding the topology of complex systems abstracted to networks is important for unraveling their functional capabilities. Many such networks follow the small-world and scale-free regimes. Several models of artificially growing networks lead to this observed network topology. Most previously proposed models for growing networks, such as rich-get-richer and duplication-divergence, produce realistic network topologies but do not consider the effects of exogenous forces such as optimization for adaptation in shaping network topology. It is likely that such forces have shaped complex systems throughout their evolution. To develop further insights into possible mechanisms that shape networks, a model that uses several previously proposed network growth algorithms was developed to grow networks that adapt under exogenous stress. A decision tree problem was used to generate a complex Boolean function. Growing networks were required to adapt to correctly decode this function using an evolutionary selection process. Under this growth regimen all growing network models are similarly adaptable. The newly added nodes tend to cluster into pathways emanating from few inputs, regardless of the growth algorithm. Distribution of redundant pathways from inputs to the output follow a power-law function with a scaling exponent (approximately 1.3). Similar distribution of redundant pathways was observed from inputs in a cell signaling network and an air traffic control network. A flat distribution of redundant pathways from inputs was observed in growing networks that do not attempt to adapt. This analysis provides initial insights into distribution of pathways in naturally evolving complex systems that have defined input-output relationships.

Checklist for law firm mergers

my hypothesis on the thinking behind this list is that libraries are organisations which are dynamic self evolving complex systems and so i have applied the simple systems theory template to how to merge two libraries. i think the key thing to remember is that systems cannot change radically or quickly and that they depend on feed-back to adjust their equilibrium. that said it follows that any merger, with its implications of big rapid change, will be difficult for any library system. it will be essential to keep open channels of communication and feed-back both within the immediate environment (the law firms themselves) and the external environment (publishers, service providers, journal subscriptions)

Book review: The economy as an evolving complex system II, edited by W. Brian Arthur, Steven N. Durlauf, and David A. Lane

This article has no abstract.

Machinic Heterogenesis and Evolution

Machinic Heterogenesis and Evolution

Book review: The economy as an evolving complex system II, edited by W. Brian Arthur, Steven N. Durlauf, and David A. Lane

Book review: The economy as an evolving complex system II, edited by W. Brian Arthur, Steven N. Durlauf, and David A. Lane

Separation of overlapping spectra from evolving systems using factor analysis. 1. Theory to obtain real spectra

A method based on factor analysis is developed that permits us to retrieve the real spectra and multiplication factors (or concentrations) of individual species in spectra of evolving complex systems with no a priori information. The application of constraints of non-negative intensities and non-negative concentrations to the matrices of abstract factors gives the unoptimized real spectra. Then searching for the most likelihood spectra by using the maximum entropy criterion gives the real spectra. Mixtures of experimental spectra are used to illustrate the method.

The Economy as an Evolving Complex System: The Proceedings of the Evolutionary Paths of the Global Economy Workshop

The Economy as an Evolving Complex System: The Proceedings of the Evolutionary Paths of the Global Economy Workshop