What Darwin Got Wrong

internal structure – as opposed to an empty-organism blank-slate account, without structure built into the hardware (structure is instead vacuumed up from input). The optimization account is thereby related to Continental rationalism; but for brain structure, rather than the more familiar mental structure”. His message is that there is a "pre-formatting" issue for evolutionary theory. Seeing neuroanatomy so intimately meshed with the computational order of the Universe brings one back, as he suggests, to the explanatory project of D’Arcy Thompson and Turing. 17 The field matured in the 1970's for microcircuit design, typically to minimize the total length of wire needed to make a given set of connections among components. Laws of form draft September 8 10 (Cherniak 2008 (in print)). There is, indeed, in our terminology, a return of the laws of form. . Some further examples are reported here. They all share the property that we have been emphasized: Evolution seems to have achieved near optimal answers to questions which, if pursued by the application of exogenous filters to solutions generated at random, as the neo-Darwinist model requires, would have imposed searching implausibly large of spaces of candidate solutions. This seems a hopeless enigma unless prior filtering by endogenous constraints are assumed. Further instances of near optimal solutions to evolutionary `problems’. The brain’s gray and white matter The segregation of the brain into gray and white matter has been shown by biophysicists to be a natural consequence of minimizing conduction delay in a highly interconnected neuronal network. A model relating the optimal brain design to the basic parameters of the network, such as the numbers of neurons and connections between them, as well as wire diameters, makes testable predictions all of which are confirmed by anatomical data on the mammalian neocortex and neostriatum, the avian telencephalon, and the spinal cord in a variety of species (of mammals and birds). (Wen and Chklovskii 2005) -Invariants of animal locomotion Scaling laws and invariants in animal locomotion have been uncovered by the engineer Adrian Bejan (Duke University) and the biologist James H. Marden (UPenn) by considering that: 18 The nearly optimal character of the genetic code is another instance. Among thousands of possible alternatives, the genetic code as we know it is optimal for minimizing the effect of frame-shift mutations and minimizing the energy wasted in synthesizing the start of anomalous protein sequences. In the words of the authors: “the universal genetic code can efficiently carry arbitrary parallel codes much better than the vast majority of other possible genetic codes”. (Itzkovitz & Alon, 2007). Laws of form draft September 8 11 “Animal locomotion is no different than other flows, animate and inanimate: they all develop (morph, evolve) architecture in space and time (self-organization, selfoptimization), so that they optimize the flow of material.” (p. 246) (Bejan and Marden 2006). Pulling together, in their model, “constructal” (sic) principles, equally applicable to the morphing of river basins, atmospheric circulation, the design of ships and submarines, and to animal locomotion, regardless of whether it consists in crawling, running, swimming or flying, they can explain the nature of the constraints and derive principles for optimized locomotion. The parameters that characterize, for each species, the locomotion that accomplishes the most for unit of energy consumed, i.e the points at the bottom of the U-shaped curve of cost versus speed, align neatly along a straight line in a logarithmic scale. Plotting optimal force against body mass, from the smallest marine creature to elephants, this straight line scales the very narrow range of speeds that maximize, for each species, the ratio of distance traveled to energy expended. Simple equations that correlate body mass, body density, body length, the gravitational acceleration and the coefficient of friction, reveal that even the distinction between flying, swimming and walking (crawling, running) is immaterial. Physical principles of optimization and simple scaling laws govern the phenomena of animal locomotion. The physics of birdsong Two physicists and a biologist, publishing in a physics journal, show that “the respiratory patterns of the highly complex and variable temporal organization of song in the canary (Serinus canaria) can be generated as solutions of a simple model describing the integration between song control and respiratory centers. This example suggests that sub-harmonic behavior can play an important role in providing a complex variety of responses with minimal neural substrate”. A straightforward generalization to other kinds of birdsongs in other species of singing birds is plausibly anticipated. (Trevisan, Mindlin et al. 2006) We want to raise the issue: have all sorts of suboptimal neuronal setups and of the ensuing suboptimal singing patterns been tried out at random over the eons and natural Laws of form draft September 8 12 selection made it so that only the optimal singers left a descendance? Did the subharmonic equations became slowly, by chance trials and selection, become encoded in the canary genes? Or are we witnessing an instance of physical optimization constraints channeling genetic, developmental and behavioral traits? -The perfect leaves In the plant kingdom, a team of American and French biologists and physicists has recently determined by means of mathematical equations and artificially generated parallel channel networks in polymeric material layers, that the scaling relations for evaporatively driven flow through simple networks reveal basic design principles for the engineering of evaporation–permeation-driven devices. These authors highlight the role of physical constraints on the biological design of leaves (Noblin, Mahadevan, Coomaraswami, Weitz, Holbrook and Zwieniecki 2008) 19 They show that the flow rate through their bio-mimetic and real leaves increases linearly with channel density until the distance between channels is comparable with the thickness of the polymer layer, above which the flow rate saturates. A comparison with the plant vascular networks shows that the same optimization criterion can be used to describe the placement of veins in leaves. Optimal foraging strategies As Von Fritsch had taught us, at the start of a foraging period, some individuals go out foraging on their own (`proactive’ searchers) and some (`reactive’ searchers) await information from returning foragers that is conveyed by the famous bee dance. The issue to be solved was: which percentage of individuals should go out and forage on their own and which percentage should wait for information (reactive searchers)? Clearly, it can’t be the case that all searchers are reactive; so the question arises whether there is an optimal percentage of proactive to reactive searchers (as a function of colony size and the availability of perishable food). Researchers (Dechaume-Moncharmont, Dornhaus et al. 19 They say: “The long evolution of vascular plants has resulted in a tremendous variety of natural networks responsible for the evaporatively driven transport of water. Nevertheless [until now] , little [wa]s known about the physical principles that constrain vascular architecture”. (page 9140) Laws of form draft September 8 13 2005) combined measurements of actual foraging behaviors with a mathematical model of the energy gain by a colony as a function both of the probability of finding food sources and of the duration of their availability. The key factor is the ratio of pro-active foragers to re-active foragers. Under specifiable conditions, the optimum strategy is totally independent (pro-active) foraging for all the bees, because potentially valuable information that re-active foragers may gain from successful foragers is not worth waiting for. This counter-intuitive outcome is remarkably robust over a wide range of parameters. It occurs because food sources are only available for a limited period. But their study emphasizes the importance of time constraints and the analysis of dynamics, not just steady states, to understand social insect foraging. The predictions of their model for optimal foraging, often quite counterintuitive, have been confirmed both in the wild and in laboratory conditions. (Dechaume-Moncharmont, Dornhaus et al. 2005). The bees appear to be “sitting” (so to speak) at the optimum of the curve of the possible ratios of proactive versus reactive foragers in a variety of situations. Once gain, we want to raise the issue: have all sorts of foraging strategies been tried out at random over the eons, and natural selection determined that only the optimal foraging bees left descendents? A progeny in which some kind of computation of the optimal ratio of proactive and reactive foragers became encoded in the genes? The question here involves multiple individuals and their behavior, and is more complex than that of the individual canaries. The issue needs to be raised nonetheless . We have seen examples where it seems that only physico-chemical and geometric constraints can explain the narrow canalizations that natural selection must have explored. The case of the bees, and two more that we are going to see (just a sample among many more in the recent literature) are such that the space of possible solutions to be explored seems too gigantic to have been explored by blind trial and error. The inference appears to be that a highly constrained search must have taken place. Accordingly, the role of natural selection may have been mostly just fine tuning. Or less. Laws of form draft September 8 14 The perfect wing-stroke The utility of one sixth or one fifth of a wing has been questioned for quite some time (including by one of us in past writing) as a challenge for gradualist adaptationism. A different tack is taken in a paper pub