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Unnatural Evolution



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One of my favorite parts of being in London is sitting in a pub or café reading The Spectator with British accents in the background, as I’m doing right now.

Matt Ridley, who is deeply knowledgeable about the topic, has a really interesting article in the current edition that sees Darwinian evolution everywhere. This is a much more frequently observed theme in British publications than anywhere else. My guess is that this is partially cheering for the home team, but that it is also not random that Darwin was British. The idea of spontaneous order without authority-from-above is a classically British notion.

It is widely accepted that analysis of human society inspired Darwin more than Darwinian biology inspired Social Darwinism, but Ridley provides fascinating evidence of correspondence to demonstrate this. But when we come to his analogy of biological evolution with technology development, we can see an important difference that Ridley doesn’t explicate in the essay:

Technology is a case in point. Although engineers are under the fond illusion that they design things, nearly all of what they do consists of nudging forward descent with modification. Every technology has traceable ancestry; ‘to create is to recombine’ said the geneticist François Jacob. The first motor car was once described by the historian L.T.C. Rolt as ‘sired by the bicycle out of the horse carriage’. Just like living systems, technologies experience mutation (such as the invention of the spinning jenny), reproduction (the rapid mechanisation of the cotton industry as manufacturers copied each others’ machines), sex (Samuel Crompton’s combination of water frame and jenny to make a ‘mule’), competition (different designs competing in the early cotton mills), extinction (the spinning jenny was obsolete by 1800), and increasing complexity (modern cotton mills are electrified and computerised).

I think that this is accurate but incomplete. Technology does advance through a process of trial-and-error, broadly conceived; but the capture of insight is often conscious, rather than being captured merely through competitive survival. Engineers observe differences in performance, and through a combination of inductive and deductive processes consciously retain advantageous differences for future generations. They also consciously manipulate future generations using insights (or, really, theories about insights) as to the relationship between the structure of the object and its performance. This is Lamarck, not Darwin.

To take one of Ridley’s examples, had early technologists literally tried random recombination after recombination of a set of parts (even making the huge assumption of the information embedded in our choice of the universe of parts and their methods of physical connection), it is highly unlikely that they would have gotten to the spinning jenny as fast as engineers and inventors actually did. You can get Shakespeare with enough monkeys on typewriters, but I don’t suggest that you put them into a writing contest against Shakespeare. Now, obviously, at some very high level of abstraction, one could argue that this process itself is the outcome of evolution, but it is in operation very different form the natural selection we see in nature.

This difference becomes striking when we come to Ridley’s description of the evolution of software:

Software inventors have learnt to recognise the power of trial and error rather than deliberate design. Beginning with ‘genetic algorithms’ in the 1980s, they designed programmes that would experiment with changes in their sequence till they solved the problem set for them. Then gradually the open-source software movement emerged by which users themselves altered programmes and shared their improvements with each other. Linux and Apache are operating systems designed by such democratic methods, but the practice has long spread beyond programmers.

I’ve done and led a lot of software development of various kinds that use both genetic algorithms (GAs) and open-source software. GAs are a highly specialized technique that are used to address a very narrow class of optimization problems – those in which we have very little information about the structure of the optimization space, and therefore, in which it is more efficient to have a search process that assumes no knowledge of the space. If we do have such knowledge, we use Linear Programs, Quadratic Programs, or any other of a huge number of alternative optimization methods. This is because these other methods assume a structure to the optimization space that (if our assumptions are correct) allows us to home in a solution much faster than does a GA. In a situation to which they are applicable, using a GA to compete with them is like using monkeys and typewriters to compete with Shakespeare.

Open Source, on the other hand, is a great case of Lamarckian, rather than Darwinian, evolution. The various engineers and release committees that create and approve incremental improvements comprise a Lamarckian process. There is a direct analogy to GAs, termed Genetic Programming, in which explicitly Darwinian methods are used to develop software code improvements. While it has been found to be useful for some very specific tasks, such as circuit design, the Open Source movement doesn’t have a lot to worry about from this competition.



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