(See other recent posts about "evolution".)
One reason why I'm skeptical of the theory of evolution is that it's complex but not very graceful. In Artificial Intelligence we often make use of evolutionary principles in computer simulations and try to encourage the emergence of complex systems by combining a simple starting state, mutations, natural selection, and time. What many computer scientists have come to realize is that this approach is often extremely limiting and rarely yields impressive results. In my research, I've yet to see a single instance of an evolutionary model that produces an output that's more complex than its input.
When asked, the computer scientist will explain that the reason the model isn't working very well is that it isn't complex enough and doesn't account for all the factors that exist in the real universe to facilitate evolution. Real biological systems are incredibly complex, as is the environment in which biological creatures live, and in our simulations we -- by necessity -- make many simplifications.
That explanation makes sense, to a point, but after a while it begs the question of how complex a system actually needs to be for evolution to occur. According to evolutionary theory, all that should be required is a genome that can undergo mutations that affect the genome's survivability, some rules to describe how one genome is selected for reproduction over another (natural selection), and a net input of energy into the system (to overcome the second law of thermodynamics). But experimentally, these components are apparently insufficient, and the excuse is generally that the system in question isn't complex enough.
This apology rubs me the wrong way. It just doesn't feel right, and it strikes me as counterintuitive. Everyone who has taken high school physics knows that physics is rather complicated as well, but when we first learn physics we make all sorts of simplifications that make our models easier to understand. We assume there's no friction. We assume every body is a point mass. We ignore relativity. We ignore electromagnetism and the strong and weak nuclear forces. And so forth. After making all these simplifications the models still work rather well, and the general principles of physics can be clearly taught and understood. The complicating factors can be introduced later and incorporated into the models as desired, and they help us get more accurate answers. It's graceful; that's why Newton's Laws served humanity so well before Einstein came around.
When it comes to evolution, however, there is no such grace. The key argument of evolution is that natural selection leads to increased organization and increased complexity, but no computer simulation has ever crossed that essential cusp. Every software system outputs less complexity than it takes in. The explanation that "it just isn't detailed enough yet" isn't very convincing because there's not even a theory addressing how complex a system has to be for evolution to "work", and the theory of evolution itself makes no such claim about required complexity. Plus, it's illogical. Why should there by some complexity threshold beyond which evolution kicks in? Or is there some other missing ingredient that we just haven't identified yet?
The other side of the argument is to point out that the natural selection rules are also part of the input to the system. In a way, evolution can be seen to "move" complexity from the rules of the universe into a genome (presumably with some significant loss due to inefficiency). The argument then says that evolution in the real universe doesn't actually output more complexity than it takes in, because the complexity of the various physical laws that govern biology are themselves part of the input. Is this something that can be measured? The human brain is the most complex system in the known universe by unit mass, but is the univserse itself more complex than human intelligence? If so, then we'll never be able to comprehend it. If not, then the argument falls apart.
That's rather frustrating.