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It's Optimal. It Must Have Evolved!


You're wandering through the countryside and you find an antenna array with a high signal-to-noise ratio. You wander some more, and find another; it looks different, but still has a near-ideal SNR. After finding half a dozen of these, you have a "Eureka!" moment: "They came about by blind, unguided processes!"

If this sounds absurd, read a paper by Garud Iyengar and Madan Rao in PNAS, "A cellular solution to an information-processing problem." From the title and description, you would think this is an intelligent design paper:

Cell-surface signaling receptors are organized into different architectures that have been arrived at multiple times in diverse contexts. To understand the trade-offs that lead to these architectures, we pose the generic information-processing problem of identifying the optimal strategy for distributed mobile noisy sensors to faithfully "read" an incoming signal that varies in space-time. This involves balancing two opposing requirements: clustering noisy sensors to reduce statistical error and spreading sensors to enhance spatial coverage, resulting in a phase transition that explains the frequent reemergence of a set of architectures. Our results extend to a variety of engineering and communication applications that involve mobile and distributed sensing, and suggest that biology might offer solutions to hard optimization problems that arise in these applications. (Emphasis added.)

These solutions "have been arrived at" -- by design? No; read the last sentence in the paper: "It is appealing that one might look to biology for insights into solutions of hard optimization problems, arrived at as a result of evolution within an information niche." Evolution did it. Give evolution the engineering design award.

It may be appealing to look to biology for insights, but it is appalling to think that solutions to hard optimization problems "arrived as a result of evolution."

One looks in vain through this paper for any indication that the authors were aware of the absurdity of their position. These are smart men; Iyengar works in industrial engineering and operations research at Columbia University. Rao tackles problems in biological physics at Raman Research Institute in Bangalor, India.

If they were to study any other signaling "architecture" that achieved a solution to a hard optimization problem, would they attribute it to chance? What goes into a conclusion that biology solved it through evolution?

Their paper actually says very little about evolution, and nothing about natural selection. It appears that scientists like this, after a lifetime's indoctrination into evolution, simply use it as a default explanation. This is evident in their first sentence:

The molecular characteristics of signaling receptors and their spatiotemporal organization have evolved to optimize different facets of information processing at the cell surface.

Since evolution, according to global scientific culture, accounts for everything in biology, no amount of evidence for design can shake that view. Additionally, no journal as eminent as PNAS is likely to tolerate any deviation from it. Given the culture, it is no longer necessary to be consistent, or even to know how Darwinian evolution works.

As a result, this paper mixes evolution with the engineering language familiar to Iyengar. He knows a lot about operations research (efficiency design). He knows optimization when he sees it. He understands competing requirements and trade-offs. But since biologists have decided how living things arose, he can dump his insights onto that default explanation and say that solutions so good that engineers might study them for insight "have been arrived at multiple times" by cells, "as a result of evolution":

Biology Solves an Optimization Problem

In this paper we show that the optimal solution to the coordinated signal estimation problem encountered by a collection of mobile sensors is determined by the trade-off between the spatial decorrelation and statistical noise in the sensors. This generic estimation problem appears naturally in a variety of engineering situations such as in communication networks and signal processing, but as discussed in the Introduction, it is the biological context that we wish to highlight here.

It is quite remarkable that this generic signal estimation problem exhibits sharp phase transitions, and that every phase has a realization in a specific cell biology context. [Some "spectacular demonstrations" are then provided.]

This is known as cognitive dissonance. If they had attributed the biological cases and the engineering cases to design, there would be consonance; the same cause, intelligence, would explain the similar results. Instead, opposite causes are assumed.

What is even more remarkable is how the information theory perspective brings out the active clustering phase that optimizes the estimation error at low densities. This organizational strategy is realized in a vast variety of signaling systems at the cell surface....

It might also be called belief in magic. Functionally, Darwinian evolution has become our culture's magician. It can accomplish any trick. No one feels it necessary to watch what goes on behind the curtain. Everyone knows evolution works wonders; after all, it changed beaks of Darwin's finches by a few millimeters, didn't it?