By Paul Cilliers
In Complexity and Postmodernism, Paul Cilliers explores the belief of complexity within the gentle of latest views from philosophy and technology. Cilliers bargains us a different method of knowing complexity and computational thought by way of integrating postmodern thought (like that of Derrida and Lyotard) into his dialogue. Complexity and Postmodernism is an exhilarating and an unique publication that are meant to be learn by means of somebody drawn to gaining a clean knowing of complexity, postmodernism and connectionism.
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Additional resources for Complexity and Postmodernism: Understanding Complex Systems
LANGUAGE AS A SYSTEM OF SIGNIFIERS ONLY Saussure introduced a system of language in which linguistic components are not assigned identity by means of rules, but derive their meaning from their relationships with all the other components. At first glance this appears to entail a fairly radical shift. However, if one considers Saussure’s insistence on both the stability of the system and the evolution of the system in a linear temporal dimension, it becomes clear that the mechanisms of the system can be given a fairly conventional description.
The heavy price paid in the process—that of averaging out the complex internal detail—is usually glossed over. To think in terms of relationships, rather than in terms of deterministic rules, is not a novelty for science, but it has always been seen as part of qualitative descriptions and not as part of the quantitative descriptions and calculations deemed necessary ever since Kepler’s insistence that ‘to measure is to know’. Many phenomena, especially in the life-sciences, but also in physics and mathematics, simply cannot be understood properly in terms of deterministic, rule-based or statistical processes.
The importance of this modelling technique for the understanding of complex systems can be summarised in the following way: • • • Neural networks conserve the complexity of the systems they model because they have complex structures themselves. Neural networks encode information about their environment in a distributed form. The notion of distributed representation undermines our understanding of conventional theories of representation. Neural networks have the capacity to self-organise their internal structure.
Complexity and Postmodernism: Understanding Complex Systems by Paul Cilliers