To make up a wrong explanation which could sound convincingly to amateurs is quite easy in any science, evolution theory included
Yes, and I was pointing out that this applies equally to biologists acting as amateur engineers.
In the retina’s case, what we are really discussing is whether the backwards retina is a suboptimal design. But to prove that, you have to prove the existence of a more optimal design. Biologists haven’t done that.
First, I have made no statement about optimality of retina, and I don’t disagree that the question may be more complicated than it seems on the first sight. In fact, it was basically my original point.
Your statement seemed to me to be a blanket substance-less dismissal of the original discussion on why the retina’s design may not be as suboptimal as it appears to amateur engineers.
Second, all designs are almost certainly suboptimal. Optimal means there is no place for improvement, and the prior probability that evolution produce such solutions is quite low.
I doubt your certainty. Optimality is well understood and well defined in math and comptuer science, and evolutionary algorithms can easily produce optimal solutions for well defined problems given sufficient time & space. Optimality in biology is necessarily a fuzzy concept—the fitness function is quite complex.
Nonetheless, parallel evolution gives us an idea of how evolution can reliably produce designs that roughly fill or populate optimums in the fitness landscape. The exact designs are never exactly the same, but this is probably more a result of the fuziness of the optimum region in the different but similar fitness landscapes than a failure of evolution.
It is also not so hard to see why humans can sometimes notice suboptimality in evolved adaptations: evolution works only by small alterations and can be easily trapped in local optimum, overlooking a better optimum elsewhere in the design space.
I think this is a mischaracterization of evolutionary algorithms—they are actually extremely robust against getting stuck in local optimums. This is in fact their main claim to fame, their advantage vs simpler search approaches.
an evolutionary adaptation that looks maladaptive to us is more likely caused by our current technical ignorance than actual maladaption
I interpret it as “we can never confidently say that any adaptation is suboptimal”,
or even “everything in nature is by default optimal, unless proven otherwise”, which is a really strong statement.
You somewhat overinterpret, and also remember that the quote is my summarization of someone else’s point. Nonetheless, I stand by the general form of the statement.
It is extremely difficult to say that a particular adaptation is suboptimal unless you can actually prove it by improving the ‘design’ through genetic engineering.
Given what we currently know, it is wise to have priors such that by default one assumes that perceived suboptimal designs in organisms are more likely a result of our own ignorance.
Do you maintain that the perceived maladaptivity of human appendix is also probably an illusion created by our insufficient knowledge of bowel engineering?
wnoise answers this for me below, and shows the validity of the prior I advocate
I agree with much of what you say, yet . .
Yes, and I was pointing out that this applies equally to biologists acting as amateur engineers.
Your statement seemed to me to be a blanket substance-less dismissal of the original discussion on why the retina’s design may not be as suboptimal as it appears to amateur engineers.
I doubt your certainty. Optimality is well understood and well defined in math and comptuer science, and evolutionary algorithms can easily produce optimal solutions for well defined problems given sufficient time & space. Optimality in biology is necessarily a fuzzy concept—the fitness function is quite complex.
Nonetheless, parallel evolution gives us an idea of how evolution can reliably produce designs that roughly fill or populate optimums in the fitness landscape. The exact designs are never exactly the same, but this is probably more a result of the fuziness of the optimum region in the different but similar fitness landscapes than a failure of evolution.
I think this is a mischaracterization of evolutionary algorithms—they are actually extremely robust against getting stuck in local optimums. This is in fact their main claim to fame, their advantage vs simpler search approaches.
You somewhat overinterpret, and also remember that the quote is my summarization of someone else’s point. Nonetheless, I stand by the general form of the statement.
It is extremely difficult to say that a particular adaptation is suboptimal unless you can actually prove it by improving the ‘design’ through genetic engineering.
Given what we currently know, it is wise to have priors such that by default one assumes that perceived suboptimal designs in organisms are more likely a result of our own ignorance.
wnoise answers this for me below, and shows the validity of the prior I advocate