Economists have labeled our economy as an information economy. With the development of information technology and the increased value of finding new and better ways to transmit and synthesize information, a theory of information has been developed. Within the realm of mathematics, theorists study the nature of information itself, using the language of axioms, theorems, and proofs to describe their work. Their results have direct and indirect applications to many other sciences and industries. The main applications have been, of course, in information technology. However, information theory also yields important evidence for the theory of intelligent design. This paper will briefly summarize the main arguments provided by information theory for intelligent design, as well as what these results mean to Christians.

Before we can begin to dig into the results of information theory, we must understand the basic foundation of the theory itself. The first step in any mathematical theory is definitions, so we will begin by defining information. According to Fred Dretske, information is not the transmission of a message, but “the actualization of one possibility to the exclusion of others” (4). For example, the fact that a flip of a coin produced a heads is the same information as the fact that the flip did not produce a tails. Drawing a queen of hearts out of a deck of cards means that none of the other fifty-one cards was drawn. When one of a set of possibilities happens, the others are ruled out. Although this definition may be much different than what we commonly refer to as information, keep in mind that the transmission of signals fits in this definition. If a certain message is conveyed across a wire, it necessarily excludes the messages that were not transmitted.

Next, an important area of information theory involves quantifying information. An intuitive answer may be to count the number of excluded possibilities, but this is not a sufficient metric (Dembski 3). As an example, let us return to the example of the deck of cards. When drawing a card from the deck, there are two possibilities: (1) the queen of hearts, and (2) everything else. However, learning that the queen of hearts was not drawn is clearly not the same amount of information as learning that the queen of hearts was drawn. Yet, according to our first metric, both possibilities have the same information value, since in each case one possibility was excluded. This example demonstrates that whatever method we use to measure information must not depend on how we choose to individuate the excluded possibilities (Dembski 3). The way to do this is to measure the probabilities of the possibilities. The probability of drawing a queen of hearts randomly from a deck of cards is approximately 0.019. The probability of selecting any other card is approximately 0.981. However, we want the numbers associated with our measurement to be greater when more information is actualized. Further, probabilities are multiplicative, not additive. The probability of drawing a queen of hearts twice in a row is 0.012 x 0.012=0.0001, not 0.012 + 0.012=0.024. To solve this problem, we take the negative logarithms of the probabilities. Further, since a convenient way to measure information is in bits, we take the logarithm base 2. Therefore, the measure of information in an event A of probability a is defined as P(A)= –log₂a (Brillouin 2).

It is also important to define the measure of information from correlated events. Let us consider a string of five bits, in which Fred and Sven each know a part. If Fred knows a string of four bits 0011, and Sven knows a string of four bits 0110, the measure of Fred’s information is 4 and the measure of Sven’s information is also 4. However the measure of the sum of their information is not the sum of the measures of their information. Together, they know the whole string of five bits, 00110, which has an information measure of 5. Therefore, the measure the sum of two sets of correlated information A and B is defined by P(A&B)=P(A)+P(B|A) (Dembski 4). By this definition, two copies of Plato’s Republic appropriately contain the same amount of information as one copy. The measure of information is also referred to as its complexity. Large amounts of information are called complex, and small amounts of information are called simple (Dembski 3).

Someone who thinks deeply about these things may protest, saying that, according to this definition, a random string of letters contains as much information as an intelligent paragraph of the same length. This is true. However, an intelligent paragraph is what information theorists call specified information. Specified information follows a pattern that is specified independent of the event, not a post hoc pattern devised to fit the event. For example, consider three archers in front of a wall so big that they must hit it. The first archer shoots an arrow at the wall. The second paints a circle on the wall, then shoots a bulls eye in the middle of the circle. The third archer shoots an arrow at the wall, then paints a circle around the arrow. The first scenario is complex information, since it was highly improbable that the arrow hit any particular part of the wall. The third is complex, patterned information, but only the second scenario is complex, specified information (Dembski 6). Further, only in the second scenario does it make sense to ask whether the archer was a skilled archer. It is important to note that unspecified information may become specified. For example, a string of coded letters is unspecified until the code is broken. The concept of specified information is relatively easy to understand intuitively, but it is difficult to formalize. More details are contained in Dembski’s The Design Inference.

Next, we will apply these ideas of information, complexity, and specificity to the genome. According to Manfred Eigen, an evolutionist, the origin of information is a serious problem for the theory of evolution. He writes, “Our task is to find an algorithm, a natural law that leads to the origin of information” (Eigen 12). Clearly, information is involved in living things. However, there is information in any random sequence of events. The information involved in life is special because it is both complex and specified.

Cells are made of proteins, and proteins are made of strings of amino acids. The twenty amino acids that are part of the proteins of living things are strung together according to information in the DNA of the cell. Each amino acid of each protein is coded by three nucleotides, and there are four different nucleotide possibilities in any one position on a string of DNA (Rana 2002). Therefore, the information in n nucleotides of DNA is 2n, since the probability of any one string occurring is 1/4ⁿ, and –log₂(1/4ⁿ)=2n. The information in one molecule of a common, relatively simple protein, iso-1-cytochrome, is approximately 250. Many other proteins are very large. One protein, chloroplast ATPase folds into a shape with a mechanical rotor, stator, and turbine (Rana 2000). To make the most simple cell, 60,000 proteins of at least 100 kinds are needed (Mastropaolo). If we assume that the proteins in a simple cell are on average as complex as iso-1-cytochrome, the information in the DNA of one simple cell is 25,000. The probability of this order of nucleotides happening randomly is one out of 5.6 x 10⁷⁵²⁵. Clearly, the information involved in life is complex.

Next, we must establish whether the information involved in life is specified. Another way to ask this question is the following: Does the information involved in life follow a path specified independently of the event of life? The pattern here is information leading to a viable, self-replicating, organism. Since it is reasonable to assume that most people recognize the difference between living things and non-living things without knowing anything about the information in the DNA of the living things, it is clear that this pattern is specified independently. Therefore, the information involved in life is both complex and specified.

Our final set of definitions is related to intelligent causes. According to Dembski, “the crucial question is how to recognize their operation” (9). Clearly, we can only recognize intelligence in discrimination, the making of a choice. Further, we recognize intelligence by the fact that some of the possible choices were not actualized. Finally, we recognize intelligence by the specification of the choice that was actualized. It is easy to understand that intelligence is recognized by discrimination, which necessarily involves actualization and exclusion. However, the specification requirement must be understood in the light of our previous discussion of specification. For example, consider two scenarios involving paper and ink. In one case, ink accidentally spills on paper. In the second, someone write a message with ink. We recognize that each scenario involves the actualization of one of many possibilities and the exclusion of others. Further, we recognize that the written message was caused by intelligence because it fits into a pattern specified independently of the event of ink marking paper. The message follows the pattern of written language. This trio of Actualization-Exclusion-Specification, according to Dembski, “constitutes a general criterion for detecting intelligence, be it animal, human, or extra-terrestrial” (9). Actualization and exclusion together guarantee that one possibility of several was chosen. However, this choice may have been by chance. Specification is how we can tell whether this contingency was directed or random.

To see how intelligence relates to complex specified information, we will consider how a researcher determines whether a rat has learned a maze. First, the researcher has to know the specific sequence of right and left turns that lead to the exit. This is the specification part. If the maze has only two turns, the probability that the rat will exit successfully, even without knowledge of the maze, is high. However, if the sequence of left and right turns is complex and the rat successfully completes the maze, the researcher may conclude that the rat has learned the maze (Dembski 10). The information actualized when the rat maneuvered the maze was both complex and specified. Thus, as Dembski states, complex specified information “pinpoints what we need to be looking for when we detect design” (11). Chance may generate complex information, and it may generate specified information, but never information that is both complex and specified.

For the most part, biologists recognize that chance cannot generate complex, specified information (Dembski 12). However, many argue that chance and necessity together generate complex, specified information. This is also false. Any combinations of trial and error must be arranged sequentially, and, since neither can generate information that is both complex and specified information, it follows that any sequence of them can’t either. This leads to the conclusion that natural causes cannot and do not generate complex, specified information.

This general conclusion has been labeled the Law of Conservation of Information, and it has several important corollaries, including the following:

“(1) The CSI in a closed system of natural causes remains constant or decreases.

(2) CSI cannot be generated spontaneously, originate endogenously, or organize itself.

(3) The CSI in a closed system of natural causes either has been in the system eternally or was at some point added exogenously.

(4) Any closed system of natural causes that is also of finite duration received whatever CSI it contains before it became a closed system.” (12-13)

Complex, specified information demands an intelligent cause. It follows that life demands an intelligent cause.

The scientists in the intelligent design movement end their argument here, and they have their own reasons to do so. However, as a Christian, information theory yields results that are more important than the fact that an unknowable, impersonal intelligence designed life. According to The Creation Trilogy, “it follows that the Creator would have a purpose in so doing, and would be capable of ordering and preserving the universe thus created, and that all creatures are under the ultimate authority of that Creator” (9).

Clearly, the God of the Bible fits the characteristics of an intelligent designer. In Proverbs 8:27-30, Wisdom personified states:

“I was there when he set the heavens in place, when he marked out the horizon on the face of the deep, when he established the clouds above and fixed securely the foundations of the deep, when he gave the sea its boundary so the waters would not overstep his command, and when he marked out the foundations of the earth. Then, I was the craftsman at his side.”

Additionally, in Jeremiah 10:20, Jeremiah writes that God “founded the world by his wisdom and stretched out the heavens by his understanding.” The Bible teaches here that God used intelligence and wisdom when he created the earth, not chance. The theory of intelligent design supports this Biblical teaching, so any support for the theory of intelligent design also supports us in our faith.

Further, in Job, God claims to have given animals their specific characteristics. He says to Job in chapter 39, verses 19-20, “Do you give the horse his strength or clothe his neck with a flowing mane? Do you make him leap like a locust, striking terror with his proud snorting?”(emphasis added). God planned each detail of organisms, and he put these details in the complex, specified information of their DNA.

In conclusion, information theory is an area of mathematics with important results for many areas of science and industry, including the theory of intelligent design. Using formal logic, information theory can show that the complex, specified information involved in life had to have an intelligent designer. As Christians, we know that this intelligent designer is the God of Bible who identifies Himself in Exodus 3:14 to Moses as “I am who I am.”

Works Cited

Brillouin, Leon. Science and Information Theory. 2^nd ed. Academic Press. New York: 1962.

Dembski, William A. “Intelligent design as a Theory of Information.” http://www.origins.org/articles/dembski_idesign2.html. 30 October 2003.

Dretske, Fred I. Knowledge and the Flow if Information. MIT Press. Cambridge: 1981.

Eigen, Manfred. Steps Toward Life: A Perspective on Evolution. trans. by Paul Wooley. Oxford University Press. Oxford: 1992.

Morris, Henry M. and John D. Morris.The Modern Creation Trilogy: Science & Creation. Vol. 2. Master. Green Forest: 1996.

Mastropaolo, Joseph. “Evolution is Biologically Impossible.” Impact. No. 317 Novemver 1999. http://www.icr.org/pubs/imp/imp-317.htm. 29 November 2003.

Rana, Fazale. “FYI: I.D. in DNA: Deciphering design in the Genetic Code.” Facts for Faith. Issue 8, 2002. http://www.reasons.org/resources/fff/2002issue08/index.shtml?main#deciphering_design. 29 November 2003.

______ “Protein Structures Reveal Even More Evidence for Design.” Facts for Faith. Issue 4, 2000. http://www.reasons.org/resources/fff/2000issue04/index.shtml?main#protein_structures_reveal_even_more_evidence_for_design. 29 November 2003.

The NIV Study Bible. Zondervan. Grand Rapids: 1995.