Embracing complexity

Jean G. Boulton, Peter M. Allen, Cliff Bowman Embracing Complexity OUP 2015

I have long thought Peter’s work important, but the necessary understanding of it has suffered from the muddled nature of complexity theories.  This book (introduction) notes the prevalence of ‘machine thinking’ and the need for a complexity worldview.  I differ from the authors in that I am unconvinced that there is a single alternative to machine thinking.  The first thing the book does, though, is to establish some concepts and a language that span key areas  and – I judge – will allow for more fruitful discussions.

As a mathematician, I consider a key issue to be the proper role of mathematics.  Some others seem to think that mathematics is a part of machine thinking, but the book makes it clear that mathematics  has provided a spur to the alternative(s). A further key insight, lacking from some other varieties of ‘complexity thinking’ is that:

 [It] is being not entirely stable, being able to wobble about, that allows the system to be resilient and almost stable.

I would add that you should always be seeking to explore and ‘test the bounds’,  even if the environment seems, at least in the short term, to reward steadier behaviours. The book also has a good characterisation (section 3.5) of a key concept, which it calls ‘tipping point’.  Unfortunately this is subtly different from usages elsewhere, which could cause confusion, but this sort of thing seems inevitable unless one invents a totally new vocabulary.  A slight criticism is that the reader is not disabused of the notion that when one tips out of one local equilibrium one must inevitably  fall into another. I am not sure that this is true. It certainly seems top be untrue that one must reach a new equilibrium in the time-frame of interest.  Turing’s notion of a catastrophic instability seems better: a catastrophe is still a catastrophe,  even if it leads to no new stability.

The book is thoroughly holistic, in the sense that is concerned with logic, reason and science.  It is thus compatible with mathematics.  It points out (4.2) that modernist views of reason and science make some questionable assumptions.  I see these as being that whatever is now should be assumed to persist.  The authors have striven to purge science etc. of such pseudo-scientific assumptions.  They take a more evolutionary view: whatever is will have evolved and may evolve some more, and may be replaced by what is not yet in being. That is, evolution is, and will persist.

With the financial crises of 2007/8 in mind, I appreciated that (5.9):

A macro-only view may be helpful if the environment and context really is stable,  but will not give information about how new features and qualities might emerge.

This resonates with Whitehead.

There is a chapter (7) on complexity and management. It uses the Myers-Briggs characterisations of ‘sensing’ and ‘thinking’ as ‘taking in information in a concrete way’ and as being ‘rational, objective’. It notes that about 70% of managers are classified in this way by Myers-Briggs tests.  I would emphasise more strongly that the notions of concreteness and objectivity are  inapplicable in all but the simplest situations, as Russell showed. In other words, this type of sensing and thinking is deluded in situations that require managing.

Chapter 10 considers the implications for economics.  Mostly obviously, prior to the crash mainstream economics assumed ‘rational agents’,  who acted as if the economy were a machine, which was hardly sensible.  The book notes:

Uncertainty is a positive quality that is a prerequisite for innovation.

Under ‘final reflections’ (11.5) it is suggested that modelling (and presumably mathematics) should be a part of reflection,  not mechanistic analysis.

Dave Marsay

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