To return briefly (or perhaps not so briefly) to an earlier point of discussion in this thread, namely whether it makes sense to speak of “optimal” in the biological context: If one insists that there are no grades of optimality (i.e., either something is optimal or it is not) then of course it is true that one cannot evaluate optimality in a biological context. But nor could one do so in any other applied science because optimality is judged by a group of criteria whose relative weights are always arguable so that there simply is no “gold standard” for it. With such a strict use of “optimal,” it is semantically (and trivially) true that the word can’t inform biological ideas.
If one is prepared to accept that some things are more optimal for a given purpose than others, then it can make good and precise sense to speak of “optimal” and “suboptimal” in a biological context.
Here’s one reason why: Engineering, in which discipline it will readily be agreed that “optimality” is a central notion (if it wasn’t, why bother doing any design and R&D work?), is a product of human endeavour. But humans are a product of the biosphere, i.e. evolution. Therefore, the discipline of engineering is ultimately a product of evolution and biological processes (as is consciousness), which enables directed design. The fact that these things are themselves products of blind processes does not negate their allowing well-defined considerations of optimality. But, as said, engineering falls within biological “design” space. In other words, the very notion of “optimality” is itself a product of evolution. Thus, it is obtuse to try to put a blanket ban on “optimality” in biology.
Here’s another reason why: Physics is permeated with optimality considerations that arise from law-like mathematical models. Physics subsumes chemistry, which has its own attendant optimality criteria that depend on the underlying physics. Chemistry subsumes biology. Biology subsumes (Darwinian) evolution. Thus, to allow optimality in physics but not in biology or evolution is to be inconsistent because physics is a superset of biology. Still, it is clear enough that levels of complexity complicate the task of evaluating optimality in biological systems, in some cases to the extent of thwarting them.
The degree of optimality of a biological configuration is gauged comparatively according to its differential success. It is true that this can normally only be done retrospectively, but so what? It’s an artefact of evolution’s undirectedness or “blindness.” It is also true that hypothesising about improved or more optimised adaptations is usually highly tricky, but again so what? That hardly implies that they are meaningless or cannot exist. We, together with our sciences, as products of biology, have learned to manipulate our environment to a significant degree so as better to suit our needs. That is a clear endeavour towards a more optimal setting that itself falls squarely in the biological sphere.
If the above arguments are not enough to convince, here are a few relevant illustrative passages from Daniel Dennett’s book Darwin’s Dangerous Idea (Simon & Schuster, 1995):
p. 103: “The contrast between the actual and the possible is fundamental to all explanation in biology. It seems we need to distinguish different grades of possibility, and Darwin provides a framework for a unified treatment of biological possibility in terms of accessibility in ‘the Library of Mendel,’ the space of all genomes.”
p. 127: “No analysis of the genomes in isolation of the organisms they create could yield [a measure for the amount and quality of biological design]. It would be like trying to define the difference between a good novel and a great novel in terms of the relative frequencies of the alphabetical characters in them. We have to look at the whole organism, in its environment, to get any purchase on the issue.”
p. 132: “So there turn out to be general principles of practical reasoning (including, in more modern dress, cost-benefit analysis) that can be relied upon to impose themselves on all life forms anywhere. We can argue about particular cases, but not about the applicability in general of the principles.” (Note: In cost-benefit analysis, solutions are always sought that are optimal as judged according to the degree to which they are able to do what is needed versus the cost of producing them. Sometimes a half-effective-but-cheap solution is good enough simply because it is very cheap.)
p. 185: “The work done by natural selection is R and D, so biology is fundamentally akin to engineering, a conclusion that has been deeply resisted out of misplaced fear for what it might imply. In fact, it sheds light on some of our deepest puzzles. Once we adopt the engineering perspective, the central biological concept of function and the central philosophical concept of meaning can be explained and united. … There are important differences, however, between the products of human engineering and the products of evolution, because of differences in the processes that create them.”
p. 194: “In the world of molecular evolution, [claims about fitness and optimality and the growth of complexity are not oversimplifications]. When [Manfred] Eigen speaks of optimality, he has a crisp definition of what he means, and experimental measurements to back him up and keep him on the straight and narrow.”
p. 223, citing Stuart Kauffman (1993): “Evolvability, the capacity to search a reasonable fraction of the [genomic] space, may be optimized when landscape structure, mutation rate, and population size are adjusted so that populations just begin to ‘melt’ from local regions of the space.”
p. 194: “[[Earlier], we saw how the engineering perspective informs research at every level from the molecules on up, and how this perspective always involves distinguishing the better from the worse, and the reasons Mother Nature has found for the distinction.”
p. 240 – 241, discussing aspects of adaptationist reasoning: “… there were those who, though they agreed with me that Gould and Lewontin had not refuted adaptationism, were eager to downplay the standard use of optimality assumptions that I claimed to be an essential ingredient in all evolutionary thinking.”
And later: “… the role of optimality assumptions in [Dan] Fisher’s work – beyond the explicit role that [Niles] Eldredge conceded – is so ‘vital’ and indeed omnipresent that Eldredge entirely overlooked it. For instance, Fisher’s inference that the Jurassic crabs swam at 15–20 cm/sec has as a tacit premise that those crabs swam at the optimal speed for their design. … Without this tacit (and, of course, dead obvious) premise, no conclusion at all could be drawn about what the actual swimming speed of the Jurassic variety was.”
'Luthon64