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This is: Studies On Slack, published by Scott Alexander on the AI Alignment Forum.
I.
Imagine a distant planet full of eyeless animals. Evolving eyes is hard: they need to evolve Eye Part 1, then Eye Part 2, then Eye Part 3, in that order. Each of these requires a separate series of rare mutations.
Here on Earth, scientists believe each of these mutations must have had its own benefits – in the land of the blind, the man with only Eye Part 1 is king. But on this hypothetical alien planet, there is no such luck. You need all three Eye Parts or they’re useless. Worse, each Eye Part is metabolically costly; the animal needs to eat 1% more food per Eye Part it has. An animal with a full eye would be much more fit than anything else around, but an animal with only one or two Eye Parts will be at a small disadvantage.
So these animals will only evolve eyes in conditions of relatively weak evolutionary pressure. In a world of intense and perfect competition, where the fittest animal always survives to reproduce and the least fit always dies, the animal with Eye Part 1 will always die – it’s less fit than its fully-eyeless peers. The weaker the competition, and the more randomness dominates over survival-of-the-fittest, the more likely an animal with Eye Part 1 can survive and reproduce long enough to eventually produce a descendant with Eye Part 2, and so on.
There are lots of ways to decrease evolutionary pressure. Maybe natural disasters often decimate the population, dozens of generations are spend recolonizing empty land, and during this period there’s more than enough for everyone and nobody has to compete. Maybe there are frequent whalefalls, and any animal nearby has hit the evolutionary jackpot and will have thousands of descendants. Maybe the population is isolated in little islands and mountain valleys, and one gene or another can reach fixation in a population totally by chance. It doesn’t matter exactly how it happens, it matters that evolutionary pressure is low.
The branch of evolutionary science that deals with this kind of situation is called “adaptive fitness landscapes”. Landscapes really are a great metaphor – consider somewhere like this:
You pour out a bucket of water. Water “flows downhill”, so it’s tempting to say something like “water wants to be at the lowest point possible”. But that’s not quite right. The lowest point possible is the pit, and water won’t go there. It will just sit in the little puddle forever, because it would have to go up the tiny little hillock in order to get to the pit, and water can’t flow uphill. Using normal human logic, we feel tempted to say something like “Come on! The hillock is so tiny, and that pit is so deep, just make a single little exception to your ‘always flow downhill’ policy and you could do so much better for yourself!” But water stubbornly refuses to listen.
Under conditions of perfectly intense competition, evolution works the same way. We imagine a multidimensional evolutionary “landscape” where lower ground represents higher fitness. In this perfectly intense competition, organisms can go from higher to lower fitness, but never vice versa. As with water, the tiniest hillock will leave their potential forever unrealized.
Under more relaxed competition, evolution only tends probabilistically to flow downhill. Every so often, it will flow uphill; the smaller the hillock, the more likely evolution will surmount it. Given enough time, it’s guaranteed to reach the deepest pit and mostly stay there.
Take a moment to be properly amazed by this. It sounds like something out of the Tao Te Ching. An animal with eyes has very high evolutionary fitness. It will win at all its evolutionary competitions. So in order to produce the highest-fitness animal, we need to – select for fitness less hard? In order to produce an animal t...
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