Authors
Ragnar Van Der Merwe
University of Johannesburg
Abstract
Paul Cilliers has developed a novel post-structural approach to complexity that has influenced several writers contributing to the current complexity literature. Concomitantly however, Cilliers advocates for modelling complex systems using connectionist neural networks (rather than analytic, rule-based models). In this paper, I argue that it is dilemmic to simultaneously hold these two positions. Cilliers’ post-structural interpretation of complexity states that models of complex systems are always contextual and provisional; there is no exclusive model of complex systems. This sentiment however appears at odds with Cilliers’ promotion of connectionist neural networks as the best way to model complex systems. The lesson is that those who currently follow Cilliers’ post-structural approach to complexity cannot also develop a preferred model of complex systems, and those who currently advocate for some preferred model of complex systems cannot adopt the post-structural approach to complexity without giving up the purported objectivity and/or superiority of their preferred model.
Keywords Paul Cilliers  Jacques Derrida  Complexity theory  post-structuralism  Connectionism  Neural networks
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Of Grammatology.Jacques Derrida - 1998 - Johns Hopkins University Press.
Of Grammatology.Jacques Derrida - 1982 - Philosophy and Rhetoric 15 (1):66-70.
Mythologies.Roland Barthes & Annette Lavers - 1973 - Journal of Aesthetics and Art Criticism 31 (4):563-564.

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