Animals need to search through
signals for the least noisy inputs.
They usually succeed quickly.

Search for a Search:
Does Evolutionary Theory Help Explain Animal Navigation?

by David F. Coppedge
Science & Culture Today, August 12, 2016

The living world is filled with searches. Moths find their mates. Bacteria find food sources. Plant roots find nutrients in the soil. Illustra’s film Living Waters includes incredible examples of search: dolphins finding prey with echolocation, salmon navigating to their breeding grounds with their exceptional sense of smell, and sea turtles making their way thousands of miles to distant feeding grounds and back home again using the earth’s magnetic field.

The subject of search looms large in William Dembski’s ID books No Free Lunch and Being as Communion. When you think about search for a moment, several factors imply intelligent design. The entity (whether living or programmed) has to have a goal. It has to receive cues from the environment and interpret them. And it has to be able to move toward its target accurately. Dembski demonstrates mathematically that no evolutionary algorithm is superior to blind search unless extra information is added from outside the system.

In the Proceedings of the National Academy of Sciences this month, five scientists from Princeton and MIT encourage a multi-disciplinary effort to understand the natural search algorithms employed by living things.

The ability to navigate is a hallmark of living systems, from single cells to higher animals. Searching for targets, such as food or mates in particular, is one of the fundamental navigational tasks many organisms must execute to survive and reproduce. Here, we argue that a recent surge of studies of the proximate mechanisms that underlie search behavior offers a new opportunity to integrate the biophysics and neuroscience of sensory systems with ecological and evolutionary processes, closing a feedback loop that promises exciting new avenues of scientific exploration at the frontier of systems biology. [Emphasis added.]

Systems biology, a hot trend in science as Steve Laufmann has explained on ID the Future, looks at an organism the way a systems engineer would. These scientists (two evolutionary biologists and three engineers) refer several times to human engineering as analogous to nature’s search algorithms. Specifically, “search research” to an engineer (finding a target in a mess of noisy data) reveals many similarities with the searches animals perform. By studying animal search algorithms, in fact, we might even learn to improve our searches.

The fact that biological entities of many kinds must overcome what appear, at least on the surface, to be similar challenges in their search processes raises a question: Has evolution led these entities to solve their respective search problems in similar ways? Clearly the molecular and biomechanical mechanisms a bacterium uses to climb a chemical gradient are different from the neural processes a moth uses to search for a potential mate. But at a more abstract level, it is tempting to speculate that the two organisms have evolved strategies that share a set of properties that ensure effective search. This leads to our first question: Do the search strategies that different kinds of organisms have evolved share a common set of features? If the answer to this question is “yes,” many other questions follow. For example, what are the selective pressures that lead to such convergent evolution? Do common features of search strategies reflect common features of search environments? Can shared features of search strategies inform the design of engineered searchers, for example, synthetic microswimmers for use in human health applications or searching robots?

The paper is an interesting read. The authors describe several examples of amazing search capabilities in the living world. Living things daily reach their targets with high precision despite numerous challenges. Incoming data is often noisy and dynamic, changing with each puff of wind or cross current. Signal gradients are often patchy, not uniform. Yet somehow, bacteria can climb a chemical gradient, insects can follow very dilute pheromones, and mice can locate grain in the dark….

Click here to continue reading.

In his book No Free Lunch, William Dembski gives an illustration of “search for a search.” Say you are on an island where treasure is buried. “Blind search” would be digging at random, hoping you will find the treasure with luck. That is highly unlikely to succeed unless you are given more information. Someone gives you a treasure map. You proceed to “X marks the spot” and dig, but don’t find it. Further investigation reveals that the map came from a closet in a nearby building with a pile of dozens of treasure maps of the island, all of them different. Now you are faced with a search through the maps to find out which one is correct. Without additional information, you are back to blind search. This process can be extended indefinitely. Dembski reasons that no evolutionary algorithm is superior to blind search. This points to the necessity of information from outside the system to reach a target. In the case of animal migration, that information had to be encoded in the animal; it could not arise from within the material makeup of the organism.

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