"Optimization pressure" makes it sound as if there is a single metric for optimization, whereas there are a constantly shifting set of different metrics. Worse (or more precisely, more complex) there are frequently multiple different "solutions" for a given metric, and evolution doesn't care. Put a little differently, there is no "optimization" pressure at all: evolution is not attempting to optimize anything (*).
Trying to fit anthropomorphized design onto a process that is absolutely the opposite of that in every way (no intent, multiple outcomes, no optimization) just leads people to not think clearly about this sort of thing.
(*) no, not even "reproductive fitness" - rates of reproduction are subject to massive amounts of environmental "noise", to the degree that minor improvements in offspring survivability will often be invisible over anything other than the very long term. Further, the most desirable rates of reproduction will also vary over time, leading to what once may have appeared to be an improvement into a liability (and vice versa, of course).
So, randomly this pathway is deleted in our species, but there won't be a satisfying "just so" explanation, it's just blind luck. I happen to think we should fix it, most people either don't care or believe we shouldn't.
Or more generally: Why did I do that specific thing? No particular reason, it just happened to work. After all, I managed not to fall off the platform for another few seconds. No telling what the future will bring.
As long as we're thinking about anthropomorphization it's amusing to note that vitamin C synthesis can be framed as a species level tragedy of the commons. In that case you are simply advocating that we as a species make the responsible choice not to participate in a race to the bottom!
> here are frequently multiple different "solutions" for a given metric
So too are there multiple different options when working towards any nontrivial goal in the real world. In the context of stochastic optimization the multi-armed bandit problem is a rather well known concept.
> evolution is not attempting to optimize anything
For the purpose of communication (of some other idea) it could be reasonable to say that the human race merely wants survival first and foremost. That is what evolution is after, at least in a sense. Of course that is not technically correct. Pointing out technical inconsistencies isn't going to convince me that I'm in the wrong here because I've already explicitly acknowledged their presence and explained why as far as I'm concerned objecting to them is simply missing the point.
Switching to a technical angle, to claim that evolution is not optimizing is to claim that water doesn't flow downhill but rather molecules just happen to vibrate and move around at random. It's completely ignoring the broader context. Evolution happens at a species level. It's an abstract concept inherently tied to other abstract concepts such as optimization and survival.
Thanks to your discussion though, I'm now wondering how to square the idea that evolution produces knowledge with the idea that it doesn't optimize even for reproductive fitness. I think you're technically incorrect there: it's that it doesn't optimize exclusively in the short term or by any one obvious strategy. The bottom line is that what survives survives, though, you can't argue with a tautology. Even if what survives is a sloth or a sleeper shark or a bristlecone or (imagine) a single infertile but incredibly tough organism, it still had to find a way (alright, stumble into a way). Maybe your objection is just that "optimize for" implies intent, but intentless-purism in language for biologists is as hard as pastless-purism in language for time travellers.
Its really fairly simply: natural selection requires two things: heritable genetics and a source of variation in the genetics between individuals. Mutation is the most basic source of variation, and that produces new information. But new information isn't necessarily knowledge. Assuming a scientific testing gloss, each new genetic code variation X can be considered as a hypothesis, that "variant X is fit", and then natural selection events that act on copies of X (for or against) serve as experiments testing the hypothesis. Through iterative experiments, we weed out the copies of the variants where the hypothesis of them being fit was proved by natural selection to be false, and what remains should be those copies of genetic variants which have (mostly) proven to be true. Learning and understanding which variants are fit (where the hypotheses are true) is knowledge, and in this way evolution produces knowledge while not having any optimization goal (in the intent sense, which I agree is a requirement for something to be meaningfully "optimizing" anything, because you can't aim in a direction without a sense for that direction).
"What do mushrooms want?" Is hilarious given your evolution context!
Sometimes, it can make sense to step back and laugh.
The number one response to words we do not like is righteous indignation.
It is almost always a bad idea too. Funny that!
Humor can be powerful as can giving benefit of doubt followed by one or more probing questions.
Amazing conversations often follow.
The key insight is that any algorithm implementation for a process which has an objective must, as an absolute minimal requirement, possess an encoding of that objective in its implementation. That is, a real representation of the goal must be in the process's make-up so that the goal can be pursued at all, because correct navigation requires assessing actions for whether they work towards the goal or not, and any such assessment requires meaningful reference to the goal. Without such a definition to refer to, differentiation between desirable and undesirable outcomes is impossible.
This goal encoding may be explicit (ie readily understandable by observers studying the implementation) or implicit (hard to parse), but either way, it must be instantiated in the make-up of the implementation, in some medium with the capacity to hold the goal definition, ie a way of storing the requisite number of bits within the implementation itself (or readily reading it from elsewhere, or constructing it from some combination thereof). This definition of the goal must be implemented in a manner that can be read and acted upon by the rest of the algorithm implementation, so that the system as a whole can pursue states that better match the goal. ie so that it can optimize.
With regards to evolution, how could nature select without having an idea of what it was selecting for? A reference definition of fitness must be available to nature if it is to measure each individual organism's fitness and select accordingly.
For a natural-selection-as-optimization-process algorithm implementation, there would need to be a component that encodes natural selection's optimization objective into the implementation's very make-up (or a ready way to read that goal from an external source).
What is the make-up of the natural selection algorithm's implementation? It is the entirety of nature itself, in whole and in part. Nature is literally everything in the universe, and literally anything in the universe, from the most massive galaxy to the smallest particle, can participate in natural selection events. And no part of nature, save for some animal brains, seems to contain a representation of a goal for natural selection.
Is it even conceivable that everything in the universe, down to the smallest particle, could encode a common goal? Does a volcano encode the goal of maximizing reproductive fitness for the populations living around it? Can a shower of cosmic rays encode the goal of making sure the creatures who's DNA it disrupts are the ones who should be removed from the populace? They don't appear to encode any such evolutionary goals, nor do they have the capacity to maintain any goal at all beyond following the physical laws of matter -- Volcanos are disordered piles of rock and churning lava, and cosmic rays are singular fundamental particles that are subject to wholesale transformation with every impact -- neither has any way of encoding a common objective for natural selection, nor is there evidence for them being able to collectively maintain one.
We can illustrate the paradox of an optimizing nature using your water molecule analogy. A collection of water molecules acting under a gravitational field will demonstrate downwards fluid dynamics which single molecules in space would not, but no matter how much H2O you put together, it will never spontaneously develop any concept of evolutionary fitness.
And yet a flash flood is a very real natural selection event that can reshape the genepool of a coastal town, but all the same it has no means of representing any goal of optimizing the population's fitness through who it drowns and who it spares; its just water. Flowing water performs natural selection, but it isn't optimizing for any goal, no matter how you try to spin it, because it has no way of maintaining a representation of a goal in its disordered and inconstant structure. It flows, yes, but it has no goal in doing so, its not pursuing any optimization objective, all the while it is a real instance of natural selection. It doesn't have or need any way of determining who is more or less fit than another, so how could it be optimizing for it? It's just flooding.
Whether its by deluge, an erupting volcano, a congenital heart attack, or a pack of rabid dogs, the processes making up natural selection events do not possess an encoding of a goal for natural selection. They do not possess the necessary information structure required to pursue a common optimization objective, and so they cannot be optimization processes in any meaningful sense.
I don't agree with this in any way, or perhaps more accurately, I don't agree that we know (and perhaps could know) the scope of the implementation even if this claim was true, which I don't think it is.
The idea that "people with a computer science background have a distinct advantage" is also plainly wrong to me. I have a background (as in, I quit my PhD in) computational biology, have been a software engineer for more than 35 years, and there are just as many people with as without computer science backgrounds who fall for the fallacy.
I’m not asking rhetorically, I’m truly interested in learning the flaws in my argument for why natural selection cannot be modelled as an optimization process. So if you have the time to reply with a more detailed rebuttal, I’d much appreciate it.
edit: Addendum: I recognize my claim that computer scientists might have an advantage in understanding this is contentious, and I was not implying that they (we) as a group have a better record of understanding evolution’s subtlety than biologists (which I studied in uni) or the average lay person. I just think they could have an advantage in understanding the version of the argument that I gave above, and I am interested in improving it for that purpose.
