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. Even in our own bodies, immune cells follow invisible cue gradients to their targets. Everywhere, from signal molecules inside cells to whole populations of higher organisms, searches are constantly going on in the biosphere.

The engineering required for a successful search, whether natural or artificial, exemplifies optimization — an intelligent design science. It’s not enough to have sensitive detectors, for instance. If too sensitive, a detector can become saturated by a strong signal. Many animal senses have adaptation mechanisms that can quench strong signals when necessary, allowing detection over many orders of magnitude. This happens in the human ear; automatic gain control in the hair cells of the cochlea gives humans a trillion-to-one dynamic range. A recent paper showed that human eyes are capable of detecting single photons! Yet we can adjust to bright sunlight with the same detectors, thanks to the automatic iris and other adaptation mechanisms in the retinal neurons.

In the PNAS paper, the authors describe additional trade-off challenges for natural search algorithms. For instance, should the organism go for the richest food source, if that will expose it to predators? Should populations with similar needs compete for resources, or divide them up? Each benefit incurs a cost. A well-designed search algorithm handles the trade-offs while maximizing the reward, even if the reward is less than ideal. Engineers have to solve similar optimization problems. They have a word for it: “satisficing” the need by reaching at least the minimum requirement. It’s obvious that achieving the best solution to multiple competing goals in a dynamic, noisy environment is a huge challenge for both engineers and animals.

The otherwise insightful paper runs into problems when it tries to evolutionize search. They say, “We expect natural selection to drive the evolution of algorithms that yield high search performance, while balancing fitness costs, such as exposure to predation risk.” Great expectations, but can they hold up to scrutiny?

1. The authors assume evolution instead of demonstrating it. They say that organisms “have evolved” strategies for searching. Because of the irreducible complexity of any system requiring sensors, detectors, interpreters and responders to pull off a successful search, this would amount to a miracle.

2. They appeal to “convergent evolution” to account for similar search algorithms in unrelated organisms. This multiplies the miracles required.

3. They speak of the environment as supplying “selective pressure” for organisms to evolve their algorithms. If the environment could pressure the formation of search algorithms, then rocks and winds would have them, too. The environment can influence the formation of a dust devil, but the whirlwind isn’t searching for anything. The environment can make rocks fall and rivers flow in certain directions, but they don’t care where they are going. It takes programming to find a target that has been specified in advance.

Most serious of all, the claim that natural selection can drive the evolution of search algorithms undermines itself. In a real sense, the scientists themselves are performing a search — a search for a search. They want to search for a universal model to explain animal search algorithms. But if they themselves are products of natural selection, then they would have no way of arriving at their own target: that being, “understanding” the natural world and explaining how it emerged.

To see why their search is doomed, see Nancy Pearcey’s article, “Why Evolutionary Theory Cannot Survive Itself.” The authors in PNAS must apply their own explanation to themselves. But then it becomes a self-referential absurdity, because they would have to say that the environment pressured them to say what they said. Their explanation, furthermore, would have no necessary connection to truth — only to survival. Remember Donald Hoffman’s debunking of evolutionary epistemology? “According to evolution by natural selection, an organism that sees reality as it is will never be more fit than an organism of equal complexity that sees none of reality but is just tuned to fitness,” he said. “Never.” Consequently, the authors of the paper cannot be sure of anything, including their claim that natural selection drives the evolution of search algorithms.

What we can say is that every time we observe a search algorithm coming into being, whether the Google search engine or a class in orienteering, we know intelligence was involved. What we never see is a new search algorithm emerging from mindless natural causes. We therefore know of a vera causa — a true cause — that can explain highly successful search algorithms in nature.

Photo credit: Illustra Media.