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No generalization is worth a damn ... including this one. |
| Feature Article - November 2019 |
| by Do-While Jones |
Occam’s Razor doesn’t prove evolution.
In last month’s newsletter, we shared George’s email in which he said, “Science has shown that the simplest explanation is almost always the correct one.” 1 Our flippant response was, “The simplest explanation isn’t necessarily correct. ‘God said it, and it happened’ is a much simpler explanation than descent with modification—but George clearly does not believe that science has proved that creation (the simplest explanation) is probably correct.” George deserved a better response than that. Here it is.
Whether he knew it or not, George was paraphrasing an idea known formally as Occam’s razor.
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Occam's razor (also Ockham's razor or Ocham's razor: Latin: novacula Occami; or law of parsimony: Latin: lex parsimoniae) is the problem-solving principle that states "Entities should not be multiplied without necessity." The idea is attributed to English Franciscan friar William of Ockham (c. 1287–1347), a scholastic philosopher and theologian. It is sometimes paraphrased by a statement like "The simplest solution is most likely the right one." but is the same as the Razor only if results match. Occam's razor says that when presented with competing hypotheses that make the same predictions, one should select the solution with the fewest assumptions, and it is not meant to be a way of choosing between hypotheses that make different predictions. 2 |
The statement, "The simplest solution is most likely the right one." is a generalization. Oliver Wendell Holmes famously stated,
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No generalization is worth a damn ... including this one. |
This brings us to a discussion of whether or not Occam’s razor is a useful generalization, and, if so, whether or not the theory of evolution really is the simplest solution.
The definition above ended with two clarifications. The second was, “it is not meant to be a way of choosing between hypotheses.” Since creation and evolution are competing hypotheses, we could state that Occam’s razor should not be used to choose between them and end the discussion right there. We won’t do that because it depends upon the dubious authority of Wikipedia to define Occam’s razor, and because we don’t want the discussion to end on a technicality. It is a topic worth discussing.
The other clarification in Wikipedia’s definition was, “Occam's razor says that when presented with competing hypotheses that make the same predictions, one should select the solution with the fewest assumptions.” Creation and evolution both predict a world full of diverse kinds of life. Occam’s razor says to select the one which uses the fewest assumptions—not which one uses the most probable assumptions. You just have to count the number of assumptions without evaluating the likelihood that they are true.
In that case, creation wins because it depends upon just one assumption: God did it. We won’t take that cheap win because (1) Occam’s razor is not meant to be a way of choosing between hypotheses; and (2) because we want an excuse to list and evaluate many of the assumptions upon which the theory of evolution is based.
Occam’s razor is used to evaluate “competing hypotheses that make the same predictions.” The various theories of evolution (Darwinian evolution, Neo-Darwinian evolution, Punctuated Equilibrium, and so on) and the various creation beliefs (Biblical Creation, Intelligent Design, Roman Mythology, and so on) are all competing hypotheses that make the same predictions; but technically, none of them is really a prediction. A pre-diction is a diction (something said) pre (before) it happens. All origin theories are explanations made after the fact. Since they aren’t really predictions, a clever lawyer could get the whole argument dismissed on a legal technicality—but we won’t do that either because it is valuable to explore the notion, “one should select the solution with the fewest assumptions.”
One might object to the “fewest” aspect because it is strictly numerical, and does not address the credibility of the assumptions. Is a hypothesis based on four perfectly reasonable assumptions inferior to a competing hypothesis based on one extremely unlikely assumption? How does one balance the number of assumptions against the likelihood of assumptions? Subjective judgment would have to be involved. This would be the case even if objective criteria were used because subjective judgment would have to be involved when establishing the objective criteria.
We can’t answer those issues—but we don’t really have to answer them. The important thing is to recognize that those issues exist because we are in the realm of philosophy and no longer in the realm of science. Science doesn’t make value judgments based on simplicity. Science determines truth through experimentation and observation—not what seems to be the simplest explanation.
Before listing all the assumptions the theory of evolution needs to be true for the theory to be correct, we must define “evolution.” We aren’t talking about minor variations in species which can be produced by selective breeding. Everyone agrees that kind of evolution is real. The controversial evolution we are addressing is, “The doctrine that unguided natural forces caused chemicals to combine in such a way that life resulted; and that all living things have descended from that common ancestral form of life.”
The first assumption this theory makes is that there was a primeval Earth, much different from Earth as it is today. That’s because, as every evolutionary scientist we know admits, life could not have evolved on Earth as it is today.
The second assumption is that there was no oxygen on that imaginary primeval Earth. That’s because oxygen would have oxidized the amino acids before they could combine to form proteins and other organic molecules necessary for life.
The third assumption is that there were some unknown, unguided natural processes which produced all the organic molecules necessary for life.
The fourth assumption is that there were some unknown, unguided natural processes which combined all those organic molecules into a living unit, presumably a cell with a membrane. (We like to call it, “Frankencell.”)
The fifth assumption is that the primeval atmosphere with 0% oxygen evolved into the modern atmosphere of 20% oxygen through some unknown process.
The sixth assumption is that Frankencell, who came to life in an oxygen-free environment, evolved into a cell that not only lives in an oxygen-rich environment, but requires oxygen to survive (through some unknown, unguided natural process). It must have had some way to harness energy that somehow involves using external energy to create carbohydrates, and uses oxidation to release that energy later on demand.
The seventh assumption is that Frankencell reproduced itself through asexual reproduction before it died. (Frankencell could not have used sexual reproduction because it was the first, and only, living cell.) If Frankencell died before reproducing, life on Earth would have gone extinct right off the bat.
The eighth assumption is that Frankencell’s species evolved from a simple single-celled species into a more complex species with differentiated cells which combined to form different kinds of organs to form interdependent systems (such as a heart, lung, and blood in a functional cardiovascular system). We are being generous to consider that to be a single assumption. We “leave it as an exercise for the student” to list all the assumptions which make up that mega-assumption because we are too lazy to do it ourselves. 
In truth, we fear that you are getting bored reading this list of assumptions. We are tired of writing them. So, we should suggest you and your friends participate in a party game. Sit around in a circle, in which each person in turn has to come up with an assumption that the theory of evolution depends upon, and continue around the circle until someone can’t think of one, or you all die of old age.
The point is that when an evolutionist, like George, tries to claim that the theory of evolution is true based on Occam’s razor, he obviously has not paused to consider all of the assumptions upon which the theory of evolution is built.
Let’s consider all the assumptions necessary to believe that eyes evolved.
All of these assumptions must be made to believe that the eye evolved. Furthermore, what caused all these things to happen? Evolutionists give survival of the fittest all the credit.
Occam’s razor depends upon the number of assumptions that have to be made—but let’s go beyond simply counting all the assumptions which must be true. Let’s consider how reasonable those assumptions are.
It would be reasonable to make the eleven assumptions about the evolution of the eye listed above if we had some assurance that they probably happened. But since none of those eleven components of a functioning vision system have ever been observed to have happened in the laboratory, or in nature, none of them are very probable.
The probability of a string of events happening is the (mathematical) product of all the individual events happening. The reason why the trifecta odds at a horse race track are so high is that, if the odds against picking the horse that comes in first is 5 to 1, and the odds against picking the horse that comes in second are 12 to 1, and the odds against picking the horse that comes in third is 8 to 1, the odds against correctly predicting the finishing order of the first three horses is 5 times 12 times 8, to 1 (480 to 1). Since every evolutionary assumption about the evolution of vision is highly unlikely, the probability of a sequence of improbable events is unimaginably small.
Some creationists compare the probability of the theory of evolution being true to the probability that a tornado in a junkyard would assemble a 747 jet aircraft—but that argument isn’t valid because it isn’t really a question of probability.
Let’s talk about the probability of kicking a field goal in American football. For our readers outside the United States, the goalposts in the National Football League (NFL) are 18.5 feet (5.6 meters) apart with a crossbar 10 feet (3 meters) above the ground—exactly the same dimensions as rugby goalposts. The ball has to be kicked between the goalposts above the crossbar to score.
Professional football players rarely miss a 10-yard field goal. Their aim can be off-center several degrees to the left or right, but the ball will still pass between the uprights. The farther away they are, the more accurate their kick has to be. As you might expect, the longer the distance, the less the probability of success is, as shown in this chart. 4
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We found this dubious chart while developing our example comparing the improbability of making a 200-yard field goal with the impossibility of making a 200-yard field goal to explain the difference between the improbability of evolution with the impossibility of evolution. We have not yet made that point; but we will soon.
Unfortunately, the questionable veracity of this chart might reflect upon the veracity of our point regarding the theory of evolution. However, it leads to the following important digression about math. Specifically, you should question mathematical models, and confirm them whenever possible—especially if they are suspicious.
There are several reasons to question this chart. The first is that points below 50 yards perfectly fit a parabola. This strongly suggests that this is not a plot of actual data—it seems to be a plot of the parabola that most closely matches the data. Would the data fit a fifth-order polynomial better? In other words, did they simply assume the curve would be parabolic, and find the parabola which fits the data best? Or did they try to fit the data to every possible equation? We don’t know. Sometimes people assume a solution and make the data fit the solution.
If I had access to the statistical data of the 10,750 NFL field goal attempts from 2002 through 2012, I would plot them as a histogram. That is, I would divide the number of field goal attempts into 5 yard bins, such as 2.5 to 7.5 yards, 7.5 to 12.5 yards, 12.5 to 17.5 yards and so on. I would call the 17.5 to 22.5 yard bin, “20-yard field goal attempts.” The histogram would show the total number of attempts, and the number of successful attempts, in each bin.
Suppose there were 400 20-yard attempts, and 320 were successful. Statistically, 80% of the 20-yard field goal attempts were successful, so one might say the probability of success is 0.8, but confusing statistics with probability isn’t always correct. Here’s why:
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The longest field goal made in [an NFL] game was 64 yards by Matt Prater of the Denver Broncos, December 8, 2013. 5 |
Granted, this is outside the 2002-2012 year range of the data used to produce this graph—but please stick with me. Not very many coaches are desperate enough to try a 64-yard field goal. Suppose our data set contained Matt Prater’s successful kick, and just one other 64-yard kick which was unsuccessful. It could be truly said that 50% of the field goal attempts from 64 yards were successful. That doesn’t mean the probability of successfully kicking a 64-yard field goal is 0.5! Teams don’t try to kick 64-yard field goals because they know the probability of success is near 0!
It is possible for an NFL kicker to make a 60-yard field goal attempt—but it is very unlikely. A mathematician might extrapolate all the data concerning field goal attempts from 40 yards out to 90 yards and compute an incredibly small probability of success for a 200-yard field goal. Let’s say they calculate the probability to be a billion-to-one.
Evolutionists sometimes argue (correctly) that given enough chances, even the most improbable event will eventually happen. Given enough kickers (thousands of kickers), and enough time (each one kicking once a minute for hundreds of years), it is mathematically possible one of them would eventually kick a 200-yard field goal even if the probability of success is a billion-to-one.
What they fail to realize is that there comes a point where theoretical probability gives way to practical possibility. Despite the billion-to-one probability calculation of a successful 200-yard field goal, it will never happen because a human simply can’t kick a football 200 yards. He just isn’t strong enough to impart enough force to kick a ball that far. The result is not governed by probability—it is constrained by physics. A 200-yard field goal isn’t improbable—it is impossible. That’s because of physics, not because of a very low probability.
Evolutionists claim that although the probability of an eye evolving by chance is remarkably low, given enough time it is bound to happen. In fact, Richard Dawkins claims it happened many times.
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It has been authoritatively estimated that eyes have evolved no fewer than forty times, and probably more than sixty times, independently in various parts of the animal kingdom. 6 |
But it isn’t a question of probability—it is a question of possibility. A tomato plant has never produced a tomato with functional eyes (despite that fact that a tomato plant can sense light and grow toward it) because it just can’t do it—not because it probably won’t do it. A tomato plant just doesn’t have the genes necessary to grow functional eyes.
Ocaam’s razor doesn’t prove evolution is true for the following reasons: (1) Every theory of evolution depends upon lots of assumptions; (2) Every one of those assumptions must be true because they are all necessary conditions; (3) Nearly all of those assumptions aren’t just wildly improbable—they are impossible because they contradict known scientific principles.
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Footnotes:
1
Disclosure, October 2019, “Extinction and Speciation”
2
https://en.wikipedia.org/wiki/Occam%27s_razor
3
http://www.anvari.org/fortune/Miscellaneous_Collections/186592_no-generalization-is-worth-a-damn-including-this-one-oliver-wendell-holmes-1809-1894.html
4
The chart was created by Decision Science News (using the R statistical programming language) by tabulating field goal successes for all 10,750 attempts by all NFL teams since 2002. https://blog.revolutionanalytics.com/2013/01/chances-of-making-an-nfl-field-goal.html
5
https://en.wikipedia.org/wiki/Field_goal#Longest_field_goal_records.
6
Richard Dawkins, 1996, Climbing Mount Improbable, chapter 5, pages 139 - 140