Wrangling Complex Systems?

Simon McGregor

Centre For Cognitive Science,

University Of Sussex

Overview

• Some examples of the ubiquity and depth of

life-like behaviour in the physical world.

• A short argument that we should try treating

complex systems as though they were

biological organisms.

• Some reflections on explanatory stances.

Examples In Natural Physical Systems

• Thermodynamics: – Self-replication intrinsically fuels entropy

production (England, 2013)

– Any ergodic system with a Markov blanket provably conducts Bayesian inference on its environment (Friston, 2013)

• Astrophysics: – Self-replicating vortex structures have been

observed in simulated star formation(Marcus et al. 2013)

• Chemistry: – Reaction-diffusion spots can exhibit

precariousness, chemotaxis and heritablevariation (Virgo et al. 2013)

– Reaction networks can conduct approximate Bayesian inference (McGregor et al. 2012)

Examples In Biosphere Systems

• Evolution:

– The replicator equation in population genetics has the same

mathematical form as Bayesian inference (Harper, 2009)

– Evolution implements processes resembling neural networks

(Watson et al., 2015)

• Technology:

– Human artefacts can be seen as actors interacting equally with

humans (Latour, …)

– Human artefacts can be seen as autopoietic (McGregor & Virgo,

2009)

• Biogeophysics:

– The entire planet Earth seems to self-regulate (Lovelock, …)

• And more…

– Social insect colonies are sometimes argued to be super-organisms

– Human social institutions can appear to have their own agenda (so

much so that companies are legally people)

Main Argument

The prevailing mentality is that “lifelike” behaviour

outside of biological organisms is a weak metaphor at

best.

• But in fact there seem to be mathematical

isomorphisms.

• We won’t know how strong the similarities really are

until we stop presuming and investigate rigorously.

(Caveat: All Non-Human Systems Are Alien)

This is not a licence to anthropomorphise. Our models of cognition shouldn’t be based on human peculiarities.

• There are big differences even between human minds.

• Organisms like insects are demonstrably not tiny people.

• “Para-organisms” (like genetic populations) are more different still.

?

Dennett’s Three Stances

Daniel Dennett points out that we can take different explanatory

“stances” towards the same physical system:

• Physical stance - understanding in terms of mechanisms;

• Design stance - understanding in terms of function;

• Intentional stance - understanding in terms of agency.

Physical & Intentional Systems

Intentional Stance

Unhelpful

Intentional Stance

Helpful

Physical Stance

Easy To Apply

Physical Stance

Hard To Apply

Philosophers

Orbits

Arbitrary Fluid

Dynamics

Hurricanes? Bacteria?

(But of course, some systems

are probably just hard to

understand.)

We are making progress on

understanding the mechanics

of certain biological systems.Immune

Systems?

And understanding the

“cognitive” abilities of others.

Population

Genetics?

We tend to ignore the life-like

properties of natural non-

biological systems.

Homing

Missiles?

We design certain artifacts to

mimic biological phenomena.

For very simple systems, the

physical stance is great.

For complex biological

systems, the intentional

stance is great.

In all the natural world, is there

really nothing in between biology

and simple mechanics?

Key Questions

Complex systems like economies and the climate are

not like vehicles we can “steer”.

• They seem to have a “life of their own” – are they

more like animals we need to “wrangle”?

We should continue using tools from the physical and

mathematical sciences to understand them.

• But, can we also use concepts from biology

(including ethology) and husbandry?

• We don’t know, because the question is taboo.