Ends and Meaning in Machine-Like Systems

Abstract

The meaning of instructions in a computer programme or of relations between parts in a functioning system is usually defined in relation to the task to be performed. However, contrary to man-made artifacts, the problem of the origin of meaning in natural systems must be approached without assuming end-seeking processes where explicit final states must be defined a priori.

In order to make the notion of meaning more precise, the theory of algorithm complexity is used to define a quantity called ‘sophistication’. A clear distinction between programme and data is used for this purpose. Sophistication as a minimum programme length is a measure of the meaningful complexity of a machine assumed to be defined by its function itself, viewed as the outcome of a computing algorithm. The metaphor of the genetic programme presumably embedded in DNA strings is discussed in this context. It is opposed to the alternative hypothesis of DNA as data processed by a programme embedded in the whole cellular biochemical machinery.

The emergence of meaningful functions in the form of classification procedures is shown to happen in functional self-organizing automata networks. This suggests the possibility of mechanisms whereby meaning can emerge in natural systems without having to define a priori the ends the systems are seeking. In such instances, the order between end and meaning seems to be reversed: meaning is created first as a classification criterion and the function is defined after or at least simultaneously in relation to it, as the classification according to that criterion.

This article was first published in Communication & Cognition, vol. 23, nos. 2–3.