When you come across an article about slime mould designing rail networks your attention is grabbed. Especially if you are, very tangentially, involved with designing railway networks. I work with a team that creates computational algorithms for routing rail and road systems. I’m just the translator, but the insight it gives me into the process is very interesting. The idea is striking and absurd, and whatever slime mould is (it’s this, apparently) it has the sort of name that fits well in absurd situations.
You might think that all the slime mould has done is, once it’s worked out the quickest way to get between the places where the food is, and how far apart they are and how much food is in each place, it’s quite easy just to go back over the short paths and everything just becomes a network of straight lines. But in fact it doesn’t. You can clearly see that the lines are more or less straight and regular, but it isn’t a question of joining every node to every other. That would be highly inefficient. The network created uses some existing nodes as waypoints to get to others, and creates new nodes when it is efficient to do so. This is what network designers do also.
If a collection of unicellular organisms with no nervous system, let alone sense organs or a brain, can do this, then, you might say, it can’t be too difficult, can it? Isambard Kingdom Brunel did it for most of the country with just a pencil and paper, and he seems to have done a pretty good job, but then he had a fully functioning brain and a team of similar organisms to work with, which always puts you ahead of the game.
The team I work with, and other teams that they work with, are busy creating new algorithms to make network design better. The problems are of unimaginable complexity, enormously greater than is generally realized. Huge quantities of data to do with terrain characteristics, land value, current demand, future demand (itself a function of the final design), and economic impact, have to be obtained to an acceptable degree of accuracy, and then analysed to find the most efficient solution. The really hard work, the clever bit where the computer engineers earn their money, is in designing algorithms that will enable you to find a good solution before the sun becomes a supernova and renders the whole exercise academic. The computational cost of these algorithms is truly astronomical, and getting it right, as opposed to nearly right, can cut millions off the cost of a project, and add many more millions to the economy of the area whose transportation it facilitates. This last factor is probably impossible to quantify, but it is undoubtedly true. Brunel had to play it by ear, and with hindsight, it could certainly have been done better.
I don’t see our road and rail networks being designed by slime mould in the future, but many heuristic algorithms are modelled on analogies with, or even direct observation of, natural processes. Experiments could easily incorporate topographical and geological features, as I imagine slime mould avoid steep hills and unsatisfactory environments the way most of us do, and a lot could be learned from observing them. Even if it only leads to the refinement of existing algorithms they will have done us a great service.
Next week: I am beaten at chess by a team of flagellate protozoa. Artificial intelligence may not be what we think it will be.