The problem with the machine model

Iain McGilchrist writes in The Matter with Things that the problem with the machine model is that we have to explain organisms from the bottom up. Organisms are unfathomably complex. McGilchrist writes,

no organism develops as the result of the execution of a sequence of predetermined steps. Each developmental ‘step’ is not simply computable from the immediately preceding one. In a classical mechanism, causation is linear and can be clearly outlined. However, in biological systems, causation tends to follow not straight lines, but spirals, involving recursive loops, and multiple causes leading to multiple effects across a network, with sometimes competing factors cross-regulating one another, reciporcally interacting, and in ways we do not understand taking information from the whole.¹

McGilchrist explains (italics in the original text):

It’s not just that steps are related in a more complicated fascion than the machine model leads us to assume. It’s the idea of there being steps at all (even if useful when focussing on the minuscule and the time-sliced) is misleading when looking at the whole over a duration of time. … It is the difference between a sequence – a concatenation, a chain – and a single, indivisible movement, a flow. Flow is a process: a chain in a series of things, that are static until one is given a push or a pul by the thing next to it. An organism is a flow, and is alive. A machine is a chain, and is dead.²

Causation is an artefact of time-slicing the flow. McGilchrist claims that “thing-thinking” is based on a “whole set of assumptions that are, to say the least, open to challenge”.³

McGilchrist reminds us, finally, that,

unlike organisms, machines, including compujters, do not operate in Gestalt fashion: when they are engineered so as to give the appearance of doing so… they do so still via rules and procedures applied to measurements, depending on trawling blindly and laboriously through vast heaps of data, in a process that speaks not of intelligence but its opposite.⁴

McGilchrist quotes Brian Ford, who writes,

the essential processes of cognition, response and decision-making inherent in living cells transcend conventional modelling, and … reveal a level of cellular intelligence that is unrecognized in science and is not amenable to computer analysis … biological systems are non-linear systems that are not amenable to digital modelling.⁵

The linearity of the machine model can no more describe the complexity of organisms than straight lines can describe the circumference of a circle, “though they may give the illusion of doing so”.⁶

Notes:
1. Iain McGilchrist, The Matter with Things: Our Brains, Our Delusions, and the Unmaking of the World, 12. The science of life: a study in left hemisphere capture, Why organisms are not machines, 3. Non-linearity
2. Ibid..
3. Ibid..
4. Ibid..
5. Brian Ford, ‘Cellular intelligence: microphenomenology and the realities of being’, Progress in Biophysics and Molecular Biology, 2017, 131, 273–87
6. Iain McGilchrist, The Matter with Things: Our Brains, Our Delusions, and the Unmaking of the World, 12. The science of life: a study in left hemisphere capture, Why organisms are not machines, 3. Non-linearity