Jupiter and Venus on camera

Jupiter and Venus were extremely bright in the sky tonight, but even so, I was surprised I got this picture with just a hand-held Pixel 4a.

18:51 GMT; bright planets, despite all the light pollution

A Crude Look at the Whole

John H. Miller.
A Crude Look at the Whole: the science of complex systems in business, life, and society.
Basic Books. 2015

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Most systems in the real world are complex: they are made of very many components, whose non-linear interactions result in high level emergent properties that are not simply related to the properties of those components. How to study such systems? Studying the components in isolation is insufficient: their interactions with each other and with their surroundings are key. Studying the whole system in detail is infeasible: there are too many components and interactions to grasp the whole in detail. So Miller advocates studying the whole as a whole. He devotes individual chapters to different facets of this whole – interactions, feedback, heterogeneity, noise, molecular intelligence, group intelligence, networks, scaling, cooperation, and self-organised criticality; and also the MCMC algorithm for studying these – explains how a degree of understanding can be obtained, and gives a range of examples, covering physical, biological, social, and economic systems.

There are many fascinating insights along the way. For example, many computer simulations of ‘complex’ systems use (relatively) homogeneous agents. Miller shows how heterogeneous agents (varying in some trait or property) can either stabilise a system against tipping, or cause it to tip more rapidly, depending on the sign of the feedback. So results from homogeneous simulations, and similarly simulations lacking noise, may give quite misleading results.

The chapter on molecular intelligence describes how complex adaptive behaviours (one might call this, intelligence) does not need a brain: complex molecular chemistry, feedback loops, and decay processes, all tuned by evolution, can deliver startling results. Intelligence can also arise from more complex agents interacting through feedback loops: the chapter on group intelligence discusses how swarms of bees can exhibit a group intelligence that no individual bee possesses.

There are also a few peculiarities. For example, in the chapter on noise, Miller discusses a search procedure for finding novel drug cocktails. Such cocktails can be much more effective than the drugs taken individually, since they can interact in non-linear ways (they are not just the sum of their individual effects); Miller states that:

[p92] drugs often interact with one another in surprising ways.

However, only a few pages later, he laments that

[p97] the Food and Drug Administration currently requires that cocktails be tested and approved as a cocktail—an extremely costly process—even when the drugs that make up the cocktail are all individually approved.

Well, yes. Because they often interact with one another in surprising ways.

The majority of the chapters discuss these processes in non-technical ways, and are the stuff of many a popular book on complex systems, although here illustrated with a wider range of examples than usual. The final chapter, however, moves into territory rarely explored in popular books: the MCMC (Markov Chain Monte Carlo) algorithm. (I think the explanation suffers a little in clarity from the understandable desire not to uses any mathematics.) He explains the genesis of the algorithm, invented to model distributions underlying certain physical processes, and how it can be used to understand some of the operations of a complex system:

[p215] The mechanisms that drive adaptive systems, such as evolution, have direct analogs to the key elements of the MCMC algorithm. In short, a complex adaptive system behaves as if it were implementing a MCMC algorithm.

That provides great potential for an explanatory and even predictive approach to complex systems of interacting components. However, I suspect that once the components have ‘true’ intelligence – can apply reasoning to their own actions and actions of others, and change their behaviours based on that reasoning – their recursive natures will make the system even more complex, and a more sophisticated explanatory approach will be needed.

Despite some minor caveats, this is an interesting book, well written, covering a lot of very deep and important concepts.

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