Simulating Complex Systems - Complex System Theories, Their Behavioural Characteristics and Their Simulation

Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. This paper focuses on the integration of methodologies in the smart adaptive applications. Statistical methods and arti cial neural networks form a good basis for the data-driven analysis of interactions and fuzzy logic introduces solutions for knowledge-based understanding the system behaviour and the meaning of variable levels. Efficient normalisation, scaling and decomposition approaches are the key methodologies in developing large-scale applications. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. The nonlinear scaling methodology based on advanced statistical analysis is the corner stone in representing the variable meanings in a compact way to introduce intelligent indices for control and diagnostics. The new constraint handling together with generalised norms and moments facilitates recursive parameter estimation approaches for the adaptive scaling. Well-known linear methodologies are used for the steady state, dynamic and case-based modelling in connection with the cascade and interactive structures in building complex large scale applications. To achieve insight and robustness the parameters are de ned separately for the scaling and the interactions. Introduction EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 2 SNE 24(1) – 4/2014 ON 1 Steady-State Modelling multiple input, multiple output MIMO response surface methodology RSM multiple input, single output MISO Fuzzy set theory Extension principle Type-2 fuzzy Figure 1: Methodologies for modelling of complex system. EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 3 O N Linguistic fuzzy models Takagi-Sugeno (TS) fuzzy models Singleton models Fuzzy relational models Figure 2: Combined fuzzy modelling. Artificial neural networks ANN Linear networks multilayer perceptron MLP backpropagation learning Neurofuzzy systems Figure 3: A fuzzy neuron. function expansion Approximate reasoning 2 Decomposition Methodologies EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 4 SNE 24(1) – 4/2014 ON Decomposition Figure 4: Decomposition for modelling. Hierarchical clustering Partitioning-based clustering algorithms fuzzy clustering Fuzzy c-means (FCM) Subtractive clustering Neural clustering Robust clustering number of clusters Composite local model Linear parameter varying (LPV) models Piecewise affine (PWA) systems Fuzzy models Multiple neural network systems mixed approach EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 5 O N 3 Adaptive Nonlinear Scaling Membership 3.1 Working point and feasible ranges Figure 5: Nonlinear scaling [28] EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 6 SNE 24(1) – 4/2014 ON 3.2 Membership definitions linguistic range linguistic values • the corner points (Figure 5) are good for visualisation; • the parameters suit for tuning; • the coefficients are used in the calculations. 3.3 Adaptation of nonlinear scaling EK Juuso Intelligent Method of Modelling and Simulation in Complex Systems SNE 24(1) – 4/2014 7 O N 4 Intelligent Systems 4.1 LE models 4.2 Hybrid LE systems EK Juuso Intelligent Methods in Modelling and Simulation of Complex Systems 8 SNE 24(1) – 4/2014 ON extension principle linguistification

Contracts as Systems

The context of our work is the complex systems simulation. Complex systems modelling and simulation require to take into account the interactions between the different components. For instance, an airport ground traffic simulation requires to model the planes with the pilots, the control tower, the

The necessity of a paradigm shift in software project management, particularly with advent of agile methods, nowadays is an interesting and challenging issue. The values and principles that have centrality in agile thought need new approaches in relation to project management. The main causes of this needed paradigm shift are unpredictability and dynamicity of software processes, and inefficiency of traditional approaches. With respect to these factors, and increasing growth of applying chaos and complex system theories in organizational study and strategic management, this research attempts to design a framework for managing agile projects based on these two theories. Concepts of these theories will be a lens to investigate software project management and propose new practices. Our analyses indicate that chaos theory can further shape strategic decisions in comparison to complex system concepts that can further help designing an appropriate agile team in the level of people interaction.

This chapter examines the combined potential of the constructs of intentionality and Complex Systems Theory, as a new theoretical direction for language learning psychology. The chapter begins with theoretical discussion of the properties of complex systems. This leads to the definition of a Complex System of Intentions, a conceptual model for understanding intentionalities that are present at individual, small group and societal levels, as well as their interrelations. Following that, key properties of the system are illustrated by juxtaposing empirical data from two research projects in Norway and Greece. First, we document the emergence of a ‘performance intentionality’ in learners’ interaction in an English L2 classroom in Norway. Next, we discuss how a ‘competition intentionality’ in a private language school in Greece emerged from interaction with the state school system, and we document its effects on language learning activity. In both cases, a data-driven analysis is used to demonstrate the emergence of the intentionalities and their generative effects, i.e., the ways in which they recursively shaped the system from which they had emerged. We conclude by revisiting the organisational openness of the system, and the processes of emergence and morphogenesis that were traced in the data, and by connecting them to Complex Systems Theory, while exploring the implications of a complexity outlook for language learning research.

Regional complex system simulation optimization through linking governance and environment performance: A case study of water environmental carrying capacity based on the SDES model

There has been a long histroy of the System Dynamics developed by J.Forrester and its analysis of complex system behaviors. It is necessary to know the detailed system structure and a large amount statistical data to build the flow diagram when using this method. However, our quantitative analysis show that the integrated model can be seen as a combination of many abstracting grey system modeles which may be decomposed from complex system. In this way, the integrated model can be simulated on a computer. The approach of combining quantitative analysis with simulation has the following features, (1) A continuous differential model is established using grey generating numbers. (2) The integrated model can be run on a computer to simulate the system behavior using DYNAMO language. In this paper, the approach of modelling the available abstracting grey dynamic systems and of simulating the integrated systems is described. The system analysis of experimental example show that grey system modelling is very useful in System Dynamics model.

The complexity of problems facing society continues to grow, and decision-makers and problem-solvers are finding many of today's emerging problems to be beyond their capability to adequately address. There is agreement in the literature that problems of this nature are complex system problems, inextricably linked to some highly complex system of systems. Establishing a clear understanding of the specific complex system context is fundamental to the process of understanding and analyzing complex systems and complex system problems across all of the different systems-based disciplines. While complex system context is widely referred to in systems literature, there is no clear characterization of exactly what system context is, making this foundational system concept ambiguous. This research addressed this gap in the systems body of knowledge by providing the needed detail and clarity to the concept of complex system context. A rigorous research methodology, employing the grounded theory method, was used to analyze data collected through a series of semi-structured interviews conducted with individuals reflecting a wide range of systems education and practical experience. Two research questions were identified as integral to increasing the understanding of context within complex systems. (1) What are the constituent elements of complex system context, and what attributes and dimensions characterize these elements? (2) What systems-based framework can be developed for constructing and articulating complex system context? Using the grounded theory method, a theory of system context was constructed, adding to the systems body of knowledge and substantiating a comprehensive and unambiguous theoretical construct for system context within complex systems. Then, based on this theory, a conceptual model to articulate and capture system-specific complex system context was developed---the Complex System Contextual Framework (CSCF). The CSCF shows significant promise for contribution to systems practitioners by supporting the future development of tools to help practitioners capture system context as a part of complex system problem formulation. The research also made a contribution in the area of research methodologies by furthering the use of the grounded theory method in the engineering management and systems engineering domain, an area where its application has been very limited.

Linguistic Geometry for autonomous navigation

'This book reflects the new level of maturity of evolutionary and institutional economics. The field of evolutionary economics has moved beyond merely describing the economy as an evolving complex system and is now developing empirically testable models of economic evolution. This book is a major contribution to the revolution now taking place in economic theory as traditional welfare economics is being replaced by more realistic models of human behavior and economic organisation.'- John Gowdy, Rensselaer Polytechnic Institute, US 'Statistical analogies and hypotheses testing now attract increasing attention in the evolutionary approach to economics. As much as evolutionary theorizing calls for new concepts to be able to go beyond static equilibrium analysis, as much does empirical work in evolutionary economics require an extension of the tool box of linear time series analysis and statistics. Foster and Holzl have brought together an excellent collection of papers highlighting both the broad range of problems facing such an extension and the possible solutions. The methodological studies and the practical applications in this volume represent a guide post for future empirical work in evolutionary economics.'- Ulrich Witt, Max Planck Institute, Jena, Germany This book takes up the challenge of developing an empirically based foundation for evolutionary economics built upon complex system theory.

Within the field of systems theory, the term robustness has typically been applied to different contexts such as automatic control, genetic networks, metabolic pathways, morphogenesis, and ecosystems. All these systems involve either man-made machines, or living organisms. In this chapter, we will consider music as a peculiar complex system, involving both the realm of machines (the musical instrument) and the realm of biology (the player and the listeners). We will discuss some of the properties of music experience in terms of different attributes of robustness, focusing in particular on stability, the property enabling a complex system to maintain its function against a wide range of external and internal changes. We will provide examples of the human ability of isolating and maintaining stable information within the perceptual flow and despite changes in the external world that reach our perceptions, leading towards a characterization of robustness in music perception as referred both to the search for regularities and to the range of tolerance that perception admits to regularities. Finally, we will list four multiple interaction cycles that typically characterize music experience and that involve both internal properties of the organism and the environment.

Complex systems theory—encompassing nonlinear dynamics, chaos theory, synergetics, dissipative structures, fractal geometry, catastrophe theory, and the like—is a young and fascinating field of scientific inquiry that spans many established disciplines (Mainzer 1996). Long before its full development from the end of the 1960s, Pierre Duhem, at the beginning of the 20th century, anticipated that unstable and complex systems pose challenging problems for scientific methodology. Citing groundbreaking works of Poincare and Hadamard, Duhem relentlessly questioned widely acknowledged, implicit assumptions in the exact sciences, such as mathematical deducibility/predictability and empirical testability. Duhem was aware that the common denominator of these challenges is instability—a fact that is well known in today’s complex systems theory. But Duhem did not proceed from that point; he restricted science to the domain of stable systems—which is in sharp contrast to recent complex systems theory. Thus, Duhem can be regarded as an interesting watershed figure on the way towards modern philosophy of complex systems.

Change in a complex system—for example, to its technology, procedures, or information flows— no matter how small, has the potential to create large effects and ripples of disruption. A complex system’s dynamics cannot be fully known, and the effects and disruptions produced by change are difficult to predict. Nonetheless, complex systems can be at least partly understood in terms of patterns in their dynamics, generalizable principles, and mechanisms of control, balance, and adaptation. This panel will focus on complex systems research and what they suggest about how to introduce change into a complex system such that the work system resilience and health are disrupted minimally. Case studies may be discussed, as well; examples of changes to established complex work systems. These changes include introducing remotely piloted aircraft systems (RPAS) into the National Airspace System (NAS), additional automation into air traffic control, and new technology into military air combat training.

The 17 Sustainable Development Goals (SDGs) are a complex system comprising 169 targets and about 230 indicators. Urban Health could be considered a complex system since it deals with 15 out of 17 SDGs, excluding the two related to life below water and life on land. According to the World Health Organization, to achieve the SDGs, countries have committed to organize Urban Health initiatives to improve the social, economic, and physical environments promoting health and sustainability globally. Cities will become more inclusive, safer and more sustainable, which are important driving forces to implement the development equation. To set up a framework to point out that Urban Health is a complex system oriented to the achievement of the SDGs. It has been conducted an extensive literature review on databases (PubMed, Embase, Web of Science and Scopus) using keywords in 3 strings combined with Boolean operators: SDGs and Urban Health, Urban Health as Complex System and Urban Health, Environmental Health and Public Health as Complex Systems. In addition, a grey literature review has been carried out. Out of 1005 publications, 21 were eventually included: 14 publications relate to the interdependent relationships between SDGs and Urban Health, with regard to the association between Urban Health and complex system, 3 publication studied the effects and implications of such correlation, 4 focused on Environmental Health and Public Health in relation with the complex system. The selected publications suggested methodologies aimed at setting up an urban health framework to achieve SDGs. There is an initial orientation focused on the study of Urban Health problems aimed at achieving SDGs from the perspective of complex systems. We highlight the need to conduct further studies on a more detailed framework in order to address this type of approach.

This introductory chapter provides an overview and a brief history of complexity science, which is the study of complex systems. All living systems and all intelligent systems are complex systems. Complexity science is relatively new but already indispensable. Many of the most important problems in engineering, medicine, and public policy are now addressed with the ideas and methods of complexity science. However, there is no agreement about the definition of 'complexity' or 'complex system', nor even about whether a definition is possible or needed. The conceptual foundations of complexity science are disputed, and there are many and diverging views among scientists about what complexity and complex systems are. Even the status of complexity as a discipline can be questioned given that it potentially covers almost everything. The origins of complexity science lie in cybernetics and systems theory, both of which began in the 1950s. Complexity science is related to dynamical systems theory, which matured in the 1970s, and to the study of cellular automata, which were invented at the end of the 1940s. By then computer science had become established as a new scientific discipline.