Editor’s note: We are delighted to welcome Steve Laufmann as a new contributor. Mr. Laufmann is a consultant in the growing field of Enterprise Architecture, dealing with the design of very large, very complex, composite information systems that are orchestrated to perform specified tasks in demanding environments.
In a recent ENV article, mathematician Granville Sewell asked an intriguing question:
In the current debate between Darwinism and intelligent design, the strongest argument made by Darwinists is this: In every other field of science, naturalism has been spectacularly successful, so why should evolutionary biology be different? Even most scientists who doubt the Darwinist explanation for evolution are confident that science will eventually come up with a more plausible explanation. That’s the way science works. If one theory fails, we look for another one; why should evolution be so different? [Emphasis added.]
Dr. Sewell’s post mainly explored entropy and theory-based reasoning. From my own perspective as an architect of large information systems, I would like to suggest a different (but complementary) answer.
Enter Information, Stage Right
Evolutionary biology was very much like other sciences up until the 1950s, when the information-bearing capabilities of DNA and RNA were discovered inside living cells.
These discoveries fundamentally changed biology. And as the information payload is increasingly unraveled, we’re seeing ever more complex and interdependent assembly instructions, activation circuits, programming sequences, and message payloads. This information is decoded and operated on by molecular machines of similar complexity, and the whole (information + machines) is self-generating, self-sustaining, and self-replicating.
The information has some intriguing properties:
It must perform an astounding number of complex functions in order to create, sustain, and replicate life. Each function requires multiple distinct programs or sequences for the various phases of its lifecycle: assembly, operation, complex orchestration with other functions, error detection and correction, replication, and so on. These are functionally distinct types of activities, so it’s almost certain that they are encoded separately, perhaps with completely different coding structures and mechanisms.
It has no value without a complex collection of molecular machines, yet it must also include the instructions for generating those same machines. The result is an immensely complex choreography of separate but interrelated information and molecular machines. Neither can function without the other — a ginormous chicken-and-egg problem.
It exhibits the design properties of the best human-engineered software systems, yet its capabilities extend well beyond any current human-engineered systems. For example, no human-engineered system is capable of self-replicating both the software that operates on the machinery and the machinery that decodes the software.
Further, based on the observed functionality in living organisms, there are many undiscovered types of information that must be present in a living cell, but which haven’t been decoded or understood yet.
Kinesin offers a fascinating example of undiscovered information in action. What programs and machinery are required to assemble the structure and function of kinesin? What information is needed for kinesin to achieve its “runtime” functions? How does kinesin know where to go to pick up a load, what load to pick up, what path to take, and where to drop its load? How does it know what to do next? All this functionality takes information, which must be encoded somewhere.
Indeed, the level of complexity is monotonically increasing, with no end in sight.
With no possibility that new discoveries will ever decrease the observed complexity, it may not be long before we see a seismic shift in the research paradigm — from the study of biological systems that happen to contain information, to the study of information systems that happen to be encoded in biology.
Causal Requirements and Causal Forces
Aside from the obvious (and intriguing) challenge of understanding the enormous complexity of life’s information payload, evolution purports to explain its origins.
The origin of life is perhaps the most obvious example of information’s formidable hurdle to evolutionary explanations. First life requires all of the following:
Sufficient complex programs and sequencing to support first life’s complete lifecycle (i.e., the directions have to be complete and correct).
Sufficient machinery to interpret the programs and to operate life (i.e., the directions must have proper effect).
Sufficient programs and machinery to replicate both the programs and the machinery (i.e., the directions must be passed to the next generation).
And all this must be present at the same time, in the same place, in at least one instant in history, at which point the whole must somehow be animated to create life. And all this must occur, by definition, before an organism can reproduce. Without reproduction, there is no possibility to accumulate function, from simple to complex, as required by evolution. Hence, the programs must have contained all the complexity required for first life at inception.
By definition, then, the minimal programs and machinery required for first life must have predated any creative capabilities (real or imagined) of Darwinian processes.
Further, since the information necessary for first life must have been assembled prior to the animation of first life, the minimal information payload must have predated first life. And it must therefore have derived from a source beyond biology as we know it.
This poses a causal quandary for evolutionary biology. For there are only two known classes of causal forces, and these have dramatically different qualities.
First, there are physical laws, which include mathematics, physics, and chemistry. These are repeatable (i.e., the same inputs always produce the same results) and purposeless (i.e., the same inputs produce the same results, no matter who gets hurt). Their repeatability makes science effective. But physical laws are not capable of acting with intent, which limits their creative capabilities.
Operating within the physical laws are random events that can change the information payload of life in various ways. But these are constrained by the same physical laws, so are similarly incapable of acting with intent. Random events cannot create complex information, except in two circumstances: (a) there is some predefined notion of a desirable outcome, and (b) any “positive gains” toward that outcome are protected from random degradation through some external mechanism. Both of these special circumstances require intention, which the physical laws cannot offer.
Second, there are intelligent causes, which are purposeful and therefore not generally repeatable. The creation of complex programming requires non-repeatability. While intelligent causes are capable of generating the right kind of information, it’s difficult to pin down when and how their actions occurred, or what their intent might have been. All sciences that deal with intelligent causes (e.g., archaeology) are made more difficult by non-repeatability.
An Impending Worldview Crisis
The search for a purposeful cause that predates biology as we know it inevitably drives the conversation to metaphysics. And this places evolution (and biology) at the center of a conflict between worldviews.
For materialists, the first class of causal force is insufficient and the second is unacceptable. Materialist biologists are thus pressed to find a third class of causal force — one that works without purpose (required to adhere to materialist philosophy), yet produces purposeful outcomes (required to adhere to the observed world). As yet no reasonable candidate forces have been proposed.
So materialists face growing dissonance between their philosophical commitment and biology’s complex programming. As the quality and quantity of the discovered interdependent programs and processing machinery increases, the plausibility of material causation gets weaker. So the materialist position is weak, and going in the wrong direction (from their perspective).
On the other hand, for anyone not fully committed to materialist philosophy the options are much more interesting. For those willing to consider the second class of causal force, things begin to fall into place and the dissonance dissipates.
For theists, the second class of causal force is not only acceptable, but expected. Further, theists are unsurprised to learn that the causal forces in class #1 are finely tuned to enable life, and they have no problem with the notion that random events are more likely to destroy information than create it (e.g., there are far more possible non-functioning programs than functioning programs).
Ongoing discoveries about the nature of the information at the core of life present a growing hurdle for the materialist worldview, but are increasingly friendly to any worldview that’s open to a pre-biological intelligencewith some means to assemble the programs and machinery minimally required for first life.
And this sets up a worldview collision.
Evolution’s Grand Challenge
Molecular biology is characterized by growing questions and shrinking answers.
It’s like the guy who, after untying his boat, finds himself with one foot on the dock and one foot in the boat. As the gap grows, it becomes increasingly hard to ignore. And uncomfortable. And temporary.
And this is evolution’s grand challenge: The complex programs and amazing molecular machines at the heart of life simply cannot be explained by any current or proposed theory of evolution, nor by any other completely material cause. Apologists for materialism cannot hide this fact much longer. Neither the volume of their arguments nor any level of vitriol can change the fact that the data is skewing against them.
Rarely has any field of science had to deal with questions so difficult, or that cut so deeply into the worldviews, minds, and hearts, of thoughtful men and women.
Evolution sits at the center of a front-and-center debate — with too much to explain, in too little time, with insufficient causal power, and with so many watching and so much at stake.
That, I would say, is what makes evolution different.
Image: � anyaberkut / Dollar Photo Club.