Complexity and Postmodernism: Understanding Complex SystemsThis work explores the notion of complexity in the light of contemporary perspectives from philosophy and science. Paul Cilliers contributes to our general understanding of complex systems, and explores the implications of complexity theory for our understanding of biological and social systems. Postmodern theory is reinterpreted in order to argue that a postmodern perspective does not necessarily imply relativism, but that it could also be viewed as a manifestation of an inherent sensitivity to complexity. As Cilliers explains, the characterization of complexity revolves around analyses of the process of self-organization and a rejection of traditional notions of representation. The model of language developed by Saussure - and expanded by Derrida - is used to develop the notion of distributed representation, which in turn is linked with distributed modelling techniques. Connectionism (implemented in neural networks) serves as an example of these techniques. Cilliers points out that this approach to complexity leads to models of complex systems that avoid the oversimplification that results from rule-based models. |
Contents
Introducing connectionism | 27 |
John Searle befuddles | 48 |
Problems with representation | 63 |
Selforganisation in complex systems | 89 |
Complexity and postmodernism | 147 |
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abstract activity algorithm analysed approach argue argument aspects basic becomes behaviour brain chaos theory Chapter characteristics of complex Chinese Room Churchland claim cognitive cognitive science complex systems components concept connectionism connectionist models context critique Derrida described determined develop différance discourses discussed distributed representation distributedness dynamics elements encode environment ethics example Fodor and Pylyshyn function groups Hilary Putnam implemented implications important input interaction interconnected internal structure interpretation language large number layer linguistic logical Lyotard machine meaning metanarrative metaphysics of presence models of complexity narrative nature neural networks neurons nodes non-linear notion organisation output patterns perform perspective philosophical possible post-structural present principle problem relationships represent result rule-based rules Saussure scientific knowledge Searle Searle's self-organised criticality self-organising systems semantic sense signifier simulated social specific Sterelny synapses theory of representation tion trace Turing Turing machine Turing test understanding values weights