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- John Collier, Change and Identity in Complex Systems.Complex systems are dynamic and may show high levels of variability in both space and time. It is often difficult to decide on what constitutes a given complex system, i.e., where system boundaries should be set, and what amounts to substantial change within the system. We discuss two central themes: the nature of system definitions and their ability to cope with change, and the importance of system definitions for the mental metamodels that we use to describe and order ideas about system change. Systems can only be considered as single study units if they retain their identity. Previous system definitions have largely ignored the need for both spatial and temporal continuity as essential attributes of identity. After considering the philosophical issues surrounding identity and system definitions, we examine their application to modeling studies. We outline a set of five alternative metamodels that capture a range of the basic dynamics of complex systems. Although Holling’s adaptive cycle is a compelling and widely applicable metamodel that fits many complex systems, there are systems that do not necessarily follow the adaptive cycle. We propose that more careful consideration of system definitions and alternative metamodels for complex systems will lead to greater conceptual clarity in the field and, ultimately, to more rigorous research.
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Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations.
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| | Other links: springerlink.com dx.doi.org | Scholar | At my library | More options ... Markets, companies and various forms of business organizations may all (we have argued) be usefully viewed through the lens of CAS -- the theory of complex adaptive systems. In this chapter, I address one fundamental issue that confronts both the theoretician and the business manager: the nature and opportunities for control and intervention in complex adaptive regimes. The problem is obvious enough. A complex adaptive system, as we have defined it, is soft assembled and largely self-organizing. This means that it is the emergent product of multiple, and often very heterogeneous, interacting factors and forces, and that the crucial interactions are not controlled and orchestrated by an overseeing executive, detailed program, or any other source of strict hierarchical control. There is thus a pressing problem -- are such systems strictly out of control and beyond the reach of useful governance? I shall argue that they are not. Such systems, though initially unfamiliar, can nonetheless be led, influenced and enabled in a variety of ways.
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| | Scholar | More options ... It is commonly accepted by those who consider macroevolution as a process decoupled from microevolution that its apparent jerkiness (and, hence, incompatibility with principles of population genetics) results from the structural complexity of epigenetic systems, since all complex cybernetic systems are expected to behave discontinuously. To analyse the validity of this assumption, the process of self-improvement has been analysed in a complex cybernetic system by means of computer simulations. It turns out that the investigated system tends to develop by accumulation of as small structural changes as possible, while larger changes are likely to result in the collapse of the system rather than in its persistence or improvement. This implies that cybernetic considerations alone cannot justify the claim that the very nature of epigenetic systems induces evolution by discrete steps rather than by gradual accumulation of small changes.
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| | Scholar | At my library | More options ... Complexly organized systems include biological and cognitive systems, as well as many of the everyday systems that form our environment. They are both common and important, but are not well understood. A complex system is, roughly, one that cannot be fully understood via analytic methods alone. An organized system is one that shows spatio-temporal correlations that are not determined by purely local conditions, though organization can be more or less localizable within a system. Organization and complexity can vary independently to some extent, but they are interconnected: organisation requires some complexity, but complexity cannot be maximum in an organized system. I will define complexity and organization more precisely, and show how these definitions imply the above properties. Next I will discuss how organized complexity can be modelled, with an eye to limitations on the tractability of both the models and the modelling process. I will finish with some remarks on the limits of our possible understanding of complexly organized systems. Keywords: complexity, organization, modelling, holism, information theory..
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| | Scholar | More options ... A complex system is generally regarded as being a network of elements in mutual interactions, which total behavior cannot be deduced from that of its parts and their properties. Thus, the study of a complex phenomenon requires a holistic approach considering the system in its totality. The aim of our approach is the design of a unifying theoretical framework to define complex systems according to their common properties and to tackle their modeling by generalizing percolation processes using pretopology theory.
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| | Scholar | More options ... The complex and dynamic nature of systems pose a particular challenge to researchers and require the use of a research methodology designed to deal with such systems. The properties of fit, relevance, understandability, generality, control, workability, generalizability, and modifiability make Glaserian grounded theory and grounded action particularly well suited for studying systems. These methods are innovative, systemic, and sophisticated enough to reveal the underlying complexities of systems and plan actions that address their complex, dynamic nature while remaining grounded in what is occurring within the systems as they change over time.
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| | Scholar | At my library | More options ... We evaluate whether John Holland’s Echo model exemplifies his theory of complex adaptive systems. After reviewing Holland’s theory of complex adaptive systems and describing his Echo model, we describe and explain the characteristic evolutionary behavior observed in a series of Echo model runs. We conclude that Echo lacks the diversity of hierarchically organized aggregates that typify complex adaptive systems, and we explore possible explanations for this failure.
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| | Scholar | More options ... In recent debates mechanisms are often discussed in the context of ‘complex systems’ which are understood as having a complicated compositional structure. I want to draw the attention to another, radically different kind of complex system, in fact one that many scientists regard as the only genuine kind of complex system. Instead of being compositionally complex these systems rather exhibit highly non-trivial dynamical patterns on the basis of structurally simple arrangements of large numbers of non-linearly interacting constituents. The characteristic dynamical patterns in what I call “dynamically complex systems” arise from the interaction of the system’s parts largely irrespective of many properties of these parts. Dynamically complex systems can exhibit surprising statistical characteristics, the robustness of which calls for an explanation in terms of underlying generating mechanisms. However, I want to argue, dynamically complex systems are not sufficiently covered by the available conceptions of mechanisms. I will explore how the notion of a mechanism has to be modified to accommodate this case. Moreover, I will show under which conditions the widespread, if not inflationary talk about mechanisms in (dynamically) complex systems stretches the notion of mechanisms beyond its reasonable limits and is no longer legitimate.
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| | Other links: philsci-archive.pitt.edu | Scholar | More options ... Using the concept of adjunctive correspondence, for the comprehension of the structure of a complex system, developed in Part I, we introduce the notion of covering systems consisting of partially or locally defined adequately understood objects. This notion incorporates the necessary and sufficient conditions for a sheaf theoretical representation of the informational content included in the structure of a complex system in terms of localization systems. Furthermore, it accommodates a formulation of an invariance property of information communication concerning the analysis of a complex system.
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| | Scholar | More options ... Using the concept of adjunction, for the comprehension of the structure of a complex system, developed in Part I, we introduce the notion of covering systems consisting of partially or locally defined adequately understood objects. This notion incorporates the necessary and sufficient conditions for a sheaf theoretical representation of the informational content included in the structure of a complex system in terms of localization systems. Furthermore, it accommodates a formulation of an invariance property of information communication concerning the analysis of a complex system.
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| | Scholar | At my library | More options ... Discussion of John Collier, Change and identity in complex systems
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