INDECS 19(4), 457-469, 2021 DOI 10.7906/indecs.19.4.1 Full text available in pdf and xml formats.

Received: 7thOctober 2021. Accepted: 8thDecember 2021. Regular article

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 article, 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

KEY WORDS
Paul Cilliers, Jacques Derrida, complexity theory, post-structuralism, connectionism, neural networks

CLASSIFICATION

JEL:

C51