The origin of species, and Everything Else: coping with evolution and religion.

In the closing scene of the classic 1960 movie Inherit the Wind, Spencer Tracy holds a Bible in one hand and Darwin's Origin of Species in the other, smiles, claps them together, and puts them both under his arm as he walks out of the courtroom. This was an iconic encapsulation of the broader consensus of educated Americans of that era that we can accept both God and Darwin. In recent decades, however, the conservative coalition has become divided over the compatibility of faith and evolution.

Many people, including many scientists, have traditionally reconciled God with evolution by considering evolution to be a process created by God. Lately, this common-sense position has been attacked as happy-talk by scientific atheists who condescendingly argue that anyone who really understands evolution realizes that it implies atheism. Probably the most famous of these is Oxford's Richard Dawkins, Professor of the Public Understanding of Science, who has expounded this idea over more than 30 years in a series of bestselling books with increasingly direct titles from The Selfish Gene to The Blind Watchmaker to, most recently, The God Delusion.

Scientific atheists put forward two propositions as logically deducible from science: that evolution eliminates the need for a Creator, and that evolution has no ultimate goal or purpose. That known science implies either conclusion is a myth, both in the sense of being objectively false, and also in the sense of being a story that a community of believers tells itself in order to provide meaning and coherence to the lives of its members. Beneath a veneer of hyper-rationality, scientific atheists often see themselves in a deeply romantic light: as uniquely courageous in the face of unpleasant facts, and as bringers-of-fire to unenlightened humanity.

Not surprisingly, many philosophers and theologians have disputed the arguments raised by Dawkins and his allies. Most of this debate, unfortunately, engages with evolution only at a very abstract or metaphorical level, which lends itself to a lot of gassy talk. In order to evaluate the claims of each group, we need to consider the mechanics of evolution.

To understand these mechanics, it's very helpful to look at an analogous system. So-called Genetic Algorithms (GAs) are computer-software implementations of the same kind of mathematics that takes place in the biological process of evolution. (Dawkins, in fact, devoted about 40 pages of The Blind Watchmaker to describing a primitive GA-type program that he wrote on his PC.) Today, GAs are widely deployed in artificial-intelligence computer programming to solve such prosaic engineering problems as optimally scheduling trucks on a delivery route or identifying the best combination of process-control settings to get maximum output from a factory.

EVOLUTION ON THE FACTORY FLOOR

Consider the example of a chemical plant with a control panel that has 100 on/off switches used to regulate the manufacturing process. You are given the task of finding the combination of switch settings that will generate the highest total output for the plant. How would you solve the problem? One obvious approach would be to run the plant briefly with each possible combination of switch settings and select the best one. Unfortunately, even in this very simplified example there are 2100 possible combinations. This is a surprisingly gigantic number--much larger, for instance, than the number of grains of sand on Earth. We could spend a million lifetimes trying various combinations of switches and never get to most of the possible combinations.

But there's a trick that can help us. Once we start to try combinations, we might begin to notice patterns like "when switches 17 and 84 are set to 'on,' production always increases when I put switch 53 to the 'off' position." Such insights could help us to narrow down our search, and get to the answer faster. This might not seem to be much help in the face of such an enormous number of possibilities, but the power of these rules is also surprising.

To illustrate this, think of a simple game: I pick a random whole number between one and a billion, and you try to guess it. If the only thing I tell you when you make each guess is whether you are right or wrong, you would have very little chance of guessing my number even if I let you guess non-stop for a year. If, however, I tell you whether each guess is high or low, there is a procedure that will get the exact answer within about 30 guesses. You should always guess 500 million first. For all subsequent guesses, you should always pick the mid-point of the remaining possibilities. If, for example, the response to your opening guess of 500 million is that you are too high, your next guess should be the mid-point of the remaining possibilities, or 250 million. If the response to this second guess is "too low," then your next guess should be the mid-point of 250 million and 500 million, or 375 million, and so on. You can find my number within about a minute.

A Genetic Algorithm works on roughly the same principle. Let's go back to our problem of the 2100 possible combinations of switch settings. We can use a Genetic Algorithm as an automated procedure to sort through the vast "search space" of possibilities--and thus home in quickly on the best one. This procedure has the same three elements as our procedure for guessing the number: a starting guess, a feedback measurement that gives some indication of how good any guess is, and an iterative method that exploits this feedback to improve subsequent guesses.

In order to establish the initial guess for the GA, imagine writing a vertical column of 100 zeroes and ones on a piece of paper. If we agree to let one="turn the switch on" and zero="turn the switch off," this could be used as a set of instructions for operating the chemical plant. The first of the hundred would tell us whether switch 1 should be on or off, the second would tell us what to do with switch 2, and so on all the way down to the 100th switch.

This is a pretty obvious analogy to what happens with biological organisms and their genetic codes--and therefore, in a GA, we refer to this list as a "genome."

Our goal, then, is to find the "genome" that will lead the plant to run at maximum output. The algorithm creates an initial bunch of guesses--genomes--by randomly generating, say, 1,000 strings of 100 zeros and ones. We then do 1,000 sequential production runs at the factory, by setting the switches in the plant to the combination of settings indicated by each genome and measuring the output of the plant for each; this measured output is termed the "fitness value." (Typically, in fact, we construct a software-based simulation of the factory that allows us to run such tests more rapidly.) Next, the program selects the 500 of the 1,000 organisms that have the lowest fitness values and eliminates them. This is the feedback measurement in our algorithm--and it is directly analogous to the competition for survival of biological entities.

[ILLUSTRATION OMITTED]

Next comes the algorithmic process for generating new guesses, which has two major components: crossover and mutation. These components are directly modeled on the biological process of reproduction. First, the 500 surviving organisms are randomly paired off into 250 pairs of mates. The GA then proceeds through these pairs of organisms one at a time. For each pair it flips a coin. If the coin comes up heads, then organism A "reproduces" with organism B by simply creating one additional copy of each; this is called direct replication. If it comes up tails, then organism A reproduces with organism B via "crossover": The program selects a random "crossover point," say at the 34th of the 100 positions, and then creates one offspring that has the string of zeroes and ones from organism Aup to the crossover point and those from organism B after the crossover point, and an additional offspring that has the string of zeroes and ones from organism B up to the crossover point and those from organism A after the crossover point. The 500 resulting offspring are added to the population of 500 surviving parents to create a new population of 1,000 organisms. Finally, a soupcon of mutation is added by randomly flipping roughly every 10,000th digit from zero to one or vice versa.

The new generation is now complete. Fitness is evaluated for each, the bottom 500 are eliminated, and the surviving 500 reproduce through the same process of direct replication, crossover, and mutation to create the subsequent generation. This cycle is repeated over and over again through many generations. The average fitness value of the population moves upward through these iterations, and the algorithm, in fits and starts, closes in on the best solution.

THE DIVINE PROGRAM

This seems like a laborious process--but it works: It helps us get the factory to very high output much faster than we could otherwise. Computer scientists were inspired to do it this way because they observed the same three fundamental algorithmic operators--selection, crossover, and mutation--accomplish a similar task in the natural world. That such a comparatively simple concept can explain so much about the way nature works is what makes genetic evolution a scientific paradigm of stupendous beauty and power. As Leonardo famously put it, simplicity is the highest form of sophistication.

And if we look closely at how a Genetic Algorithm works in the factory, we can glean some important insights about how evolution works in nature--insights that have a direct bearing on the continuing controversy about God and evolution.

Notice, first of all, that our factory "evolution" process did not begin ex nihilo. It required preexisting building blocks--both in the form of an initial population and in the underlying rules of the algorithm itself.

Notice, second, that one of the 2100 possible combinations of switch settings will produce the highest output, and with enough time the algorithm will always converge on this one answer. The algorithm is therefore the opposite of goalless: It is, rather, a device designed to tend toward a specific needle in a haystack--the single best potential result.

Notice, third and finally, that though the number of possible solutions is very large, it is finite. With sufficient computational power the goal is, in principle, knowable without ever running the algorithm. The algorithm itself is just a computational convenience.

These three observations will be very helpful to us as we evaluate the myths of scientific atheism.

First, it is obvious from the factory analogy that evolution does not eliminate the problem of ultimate origins--of what, in explicitly religious terms, is called Creation. Dawkins himself, in The Blind Watchmaker, is clear about the fact that evolution requires preexisting building blocks. He writes: "The physicist's problem is the problem of ultimate origins and ultimate natural laws. The biologist's problem is the problem of complexity. The biologist tries to explain the workings, and the coming into existence, of complex things, in terms of simpler things. He can regard his task as done when he has arrived at entities so simple that they can safely be handed over to the physicists."

Dawkins, then, has punted the problem to the physicists. Specifically, he cites The Creation, a book by Oxford physics professor Peter Atkins that addresses this question. Dawkins says that Atkins claims the original units of creation do not demand anything as grand as a Creator. But Atkins has come to have second thoughts. In a speech in Edinburgh earlier this year, Atkins had this to say: "I must admit that we simply do not know how the universe can come into being without intervention."

This is where the game of pass-the-parcel winds up in a dead end--as, eventually, it must. A scientific theory is a falsifiable rule that relates cause to effect. If you push Dawkins and company far enough, you find yourself more or less where Aristotle was more than 2,000 years ago in stating his view that any chain of cause-and-effect must ultimately begin with an Uncaused Cause. No matter how far science advances, an explanation of ultimate origins must always--by the very definition of the scientific method--remain a non-scientific question.

A second prominent myth of scientific atheism is that evolution has no goal. Quite the opposite, of course, was true of the algorithm we used to optimize the factory. Evolution in nature is more complicated--but the complications don't mean that the process is goalless, just that determining this goal would be so incomprehensibly hard that in practice it falls into the realm of philosophy rather than science.

One important complication is that evolution in nature proceeds against a more complex fitness function than "see how much output this factory creates." The natural fitness landscape is defined by survival and reproduction, and it is constantly changing as the environment changes--for example, as new species arise or the climate becomes colder. It is prohibitively difficult to calculate the result of this process--but it is, in principle, calculable; the fitness landscape, after all, is only the product of the interaction of other physical processes.

A second major complication is that genetic strings in nature have complex structures and can evolve to some arbitrary length--unlike our factory example, where the simple "genetic string" had a fixed length of 100 positions. But, even in nature, the string must always have a finite length, as regulated by physical laws; and therefore the total number of potential combinations of genetic components remains finite. Dawkins says repeatedly that the number of possible genetic combinations is "all but infinite," but this is just a very loaded way of saying "finite."

The combination of a constantly changing fitness landscape and an extraordinarily large number of possible genomes means that scientists appropriately proceed as if the goal of evolution were incalculable, while from a philosophical perspective it remains calculable in principle, using only the information embedded in the initial conditions.

And here it's especially important that we be clear about our terms. The scientific atheists sweep a lot of philosophical baggage into the term "random": It is often used loosely to imply a senselessness, a basic lack of understandability, in natural occurrences. But in fact, even the "random" elements of evolution that influence the path it takes toward its goal--for example, mutation and crossover--are really pseudo-random. For example, if a specific mutation is caused by radiation hitting a nucleotide, both the radiation and its effect on the nucleotide are governed by normal physical laws. Human uncertainty in describing evolution, which as a practical matter we refer to as randomness, is reducible entirely to the impracticality of building a model that comprehensively considers things such as the idiosyncratic path of every photon in the universe compounded by the quantum-mechanistic uncertainty present in fundamental physical laws that govern the motion of such particles. As a practical matter, we lack the capability to compute either the goal or the path of evolution, but that is a comment about our limitations as observers, not about the process itself.

Accepting evolution, therefore, requires neither the denial of a Creator nor the loss of the idea of ultimate purpose. It resolves neither issue for us one way or the other.

But evolution has been empirically associated with materialism for a reason: It undermines some traditional religious notions. Contemplating a Creator who acts through a process as multi-layered as evolution tends to lead us to see the spiritual world in an increasingly abstract light. The risk to religion is that this accommodation can begin an inexorable process that leads to a theology so attenuated that it becomes vanishingly close to materialism.

Fortunately, it is possible to thread the intellectual needle: to defer to scientific explanations for non-ultimate physical processes, while still remaining within the central Judeo-Christian tradition.

One of the advantages of institutionalized religion is that it conserves insight. Ironically, dealing with evolution places us back in the company of Augustine and Aquinas, who were both forced to figure out how to reconcile powerful proto-scientific ideas with Christianity. They described God as acting through laws or processes. In about the year 400, Augustine described a view of Creation in which "seeds of potentiality" were established by God, which then unfolded through time in an incomprehensibly complicated set of processes. In the 13th century, Aquinas--working with the thought of Aristotle and Augustine--identified God with ultimate causes, while accepting naturalistic interpretations of secondary causes. Neither Augustine nor Aquinas was a proto-Darwinist: Augustine, for example, thought species were immutable. What is striking about both of them, however, is their insistence on understanding and incorporating the best available non-theological thinking into our religious views.

Relying on this deep intellectual heritage, most major denominations in the Western world have accepted evolution as fully consistent with theistic religious faith. Thoughtful conservatives would be wise to agree.

Mr. Manzi is the CEO of an applied-artificial-intelligence software company.