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Lack of Signal Is Not a Lack of Information


In his book No Free Lunch, Bill Dembski demonstrated that no evolutionary algorithm is superior to a random search. Yet many animals catch prey with what appears to be a random walk through a noisy environment. How do they do it?

A biologist and a mathematician from the University of Florida tackled the problem of modeling animal search behavior. Publishing in PNAS July 9, Hein and McKinley noted that "Many organisms locate resources in environments in which sensory signals are rare, noisy, and lack directional information." Yet the animals find their food, even when appearing to wander aimlessly. In "Sensing and decision-making in random search," the authors extended mathematical models about random search in these situations and showed that it is not really random.

To understand the problem, play shark for a moment. You're swimming around in dark and murky water, with no directional clues to tell you where you are or have been. You have eyes and a nose, and you're hungry, but at the moment you can't see anything or smell anything. What is the best strategy to find the nearest prey? Remember, to play off Dembski's book title, there's "no free lunch" out there. You need to search for it. This problem is faced by most animals, from the albatross to the moth, the bat, the sea turtle, or the amphibian waiting in a river current for something to pass by.

A random search relies on pure dumb luck. This might be your lucky day as a shark, but it might not. Since you can't even tell where you've been, so you might keep retracing the same fruitless steps. What you need is information. That's what Hein and McKinley added to their model: information can be found even in noise:

Our results show that including even a simple response to noisy sensory data can dominate other features of random search, resulting in lower mean search times and decreased risk of long intervals between target encounters. In particular, we show that a lack of signal is not a lack of information. Searchers that receive no signal can quickly abandon target-poor regions. On the other hand, receiving a strong signal leads a searcher to concentrate search effort near targets. These responses cause simulated searchers to exhibit an emergent area-restricted search behavior similar to that observed of many organisms in nature.
Back in your shark suit, you notice that as you go in one direction, there's no sight or smell of prey. This "lack of signal" constitutes useful information: now you know it's a poor place to search. You turn around and go for a certain "step length" in another direction. The new direction might also be unproductive, but it's better than a random search. With this slightly improved search strategy, even just a very slight signal can help you focus on a new direction, and eventually win you a well-deserved lunch.

Hein and McKinley also found that information from non-directional cues, like smell (olfaction), helps an animal converge on its prey more quickly than directional cues such as vision alone. It may not be possible to tell where a smell is coming from, but by moving, the shark or moth can sense a gradient -- whether the smell gets weaker or stronger -- and respond accordingly. Obviously, the more sources of information, the better.

Animals possess an astonishing array of sensory equipment to aid them in their searches. In addition to the "five senses" with which we are all familiar, bats and dolphins have sonar, some birds and turtles can sense the earth's magnetic field, platypuses and some fish can sense electric fields. The responsiveness of these senses in some species is astonishing: moths can sense their mates from a mile away by the signal that a single pheromone molecule triggers in their antenna, and salmon can detect smells from their native streams in parts per billion. Ants drive us crazy with their ability to locate one stray speck of food left on the countertop and tell all their friends.

Each of these senses adds information to a random search, allowing the animal to dramatically reduce search time. Memory is another aid; if you can remember where you have been, you can avoid wasted time even in a random search. A western bird, the Clark's nutcracker, is known to be able to recall after a year where it has stashed up to 33,000 seeds in separate locations. Hein and McKinley show us that even the absence of signal adds information. Information is the opposite of randomness.

A major point of Dembski's No Free Lunch (Rowman and Littlefield, 2002) is that so-called "evolutionary algorithms" (concocted to help evolution build ants, albatrosses, sharks and bats from scratch by random mutation and natural selection) violate the No Free Lunch (NFL) Theorems, a set of mathematical theorems about such algorithms, proved over a decade ago. Dembski explained why these theorems doom all evolutionary algorithms to failure:

The upshot of these theorems is that averaged over all possible fitness functions, no search procedure outperforms any other. It follows that any success an evolutionary algorithm has in outputting specified complexity must ultimately be referred to the fitness function that the evolutionary algorithm employs in conducting its search. The No Free Lunch theorems dash any hope of generating specified complexity via evolutionary algorithms.
The reason is that a random search is itself an evolutionary algorithm. It's like the null set in set theory. It may be null, but it's a set nonetheless. Consequently, no evolutionary algorithm outperforms a random search. Evolutionists cheat by sneaking extra information in the side door -- a technical foul for an unguided process like neo-Darwinism.

In a memorable illustration, Dembski hands you a shovel on an island where there is known to be a buried treasure. To start with, you have no idea where the treasure is, so you dig at random. But then he gives you a treasure map. He has just given you valuable information, sending you gleefully to excavate where the X marks the spot. Unfortunately, he tells you this is just one treasure map out of many. He takes you to a closet full of treasure maps. "Which one is the right one?" you ask. You need further information. He promises you that a key to the treasure maps is in another closet. You open that closet and it's full of keys to the treasure maps. "Which one is the right one?" you ask again, and...you get the picture. It's an infinite regress. You're no better off than when you started.

Without specific information, therefore, no search strategy will outperform a random search. On your treasure island, even the lack of signal is better than no signal at all. If Dembski played "Blind Man's Bluff" with you as you dug, telling you "You're getting colder, colder, you're freezing!" you would at least know to turn around.

But now consider a treasure island thousands of miles across, with quadrillions of treasure maps and keys. Without very specific information, or initial information that can lead to better information, your search will be as hopelessly futile as a blind search. On a very small island, by contrast, you might get lucky. The success of a random search, in other words, is a function of the search space.

What is the search space for the first living cell? Dembski shows that the set of functional proteins or genes is a tiny, tiny fraction of the set of all possible sequences of amino acids or DNA bases -- so small, in fact, that getting a cell by chance under the best possible conditions falls far, far outside the universal probability bound (UPB) of one chance in 10 to the 150th power. It's virtually impossible anywhere in the universe at any time. Blind searching is not an option, consequently -- but that's what any and every evolutionary algorithm reduces to.

The fact that animals succeed at apparently random searches for their prey in murky environments where any signal is rare, noisy or non-directional indicates that information is being detected even in the noise, information they can process to update their search strategy, away from an initial blind search to a better-informed algorithm. Hein and McKinley remind us that a lack of signal is not a lack of information. We should remember, though, that any "signal" is meaningless without an organ or system to recognize its information content.

This supports ID theory at two levels: (1) First, it shows that the animals' sense organs do not employ evolutionary algorithms, but are finely tuned to detect and process additional information, however scant it might be (even a lack of signal). (2) Second, it shows that the animals themselves did not originate via evolutionary algorithms, because an evolutionary algorithm cannot produce an algorithm better than itself (which amounts to a blind search).

Putting it all together:

  • The NFL Theorems show that evolution is stuck with a blind search.
  • Information lights the path out of blind search; the more information, the brighter the light.
  • Complex specified information (CSI) exceeds the UPB, so in the evolutionary context a blind search is not an option.
  • Our uniform experience with CSI is that it always has an intelligent cause.
  • Evolution is disconfirmed by negative arguments (NFL theorems and the UPB).
  • Intelligent design is confirmed by positive arguments (uniform experience and inference to the best explanation).