Meyer presents another set of predictions that relate to the “structure, organization, and functional logic of living systems.” This is the first of four predictions in this field.

Several chapters of the book are devoted to analyzing proposed origin-of-life theories. One of the more popular scenarios in the last decade is the so-called “RNA world scenario.” The dilemma facing origin of life researchers is to decide what elements evolved first. Since proteins and enzymes that are essential to replicating DNA are themselves formed from patterns stored in DNA, it is not clear what came first, the DNA chicken or the protein egg. The RNA world seeks to avoid that dilemma by postulating that the first biomolecules to evolve were RNA fragments that were self-catalytic. These molecules evolved to proteins and DNA later on. Meyer is simply stating that none of the proposed RNA catalysts will prove to be up to the task.

This prediction is more likely than the first two to have an experimental resolution within our lifetimes. No doubt, the RNA world scenario will be revised significantly as time goes on, but it is reasonable to expect that the core concept of self-catalytic processes may be rejected or shown to be plausible. The definition of plausibility may make it hard to clearly judge whether the prediction has been met or not. What is judged by some to be plausible may not be to others. Some might argue that plausibility has already been established. Nevertheless the prediction has some merit.

The validity of ID, however, is not likely to be influenced by the result. Again, the prediction of a null result is quite problematic. If RNA catalytics are not up to the task, it is still quite possible that other molecules will be discovered to play that role. There are many reasons why this null result might occur. On the other hand, if they are up to the task that Meyer poses, few ID advocates would give up the faith. There are yet many other aspects to be solved in order to have plausible theories for the origin of life. In summary, the prediction may or may not be verified but either way, the argument will go on.

This is closely related to the first prediction, extending it to the realm of computer simulations of evolution as well as the real world of physical processes. The idea is that even a computer program designed to imitate evolution by introducing mutations and a selection algorithm is unable to generate significant complex information without an intelligent agent inserting it in some way.

This prediction is a generalization of the critique that Meyer offers, on pages 283-290, of Ev and Avida, two computer simulation programs that have been published. These programs attempt to model the processes of mutation and natural selection. Meyer’s critique is based on the analysis done by Robert Marks. The claim is that Ev achieves its success largely due to the introduction of so-called “active information” by which Marks means information added by the programmer to reduce the difficulty of the search. The analysis of Avida is more complex. Meyer’s detailed note on pages 533-535 chides Avida more for failing to realistically simulate the generation of new functional genetic information than for supplying too much active information.

It is inevitable that simulating hundreds of millions of years of evolution requires some degree of incorporation of “active information.” Accounting rules for such input are not well established. Settling this prediction is more likely to shed light on the intricacies of simulating real-world situations rather then settling any issues of ID. I invite readers who may have worked with or studied Ev and Avida or similar simulations to comment and enlighten us on more details.

This prediction relates to the causal powers of materialistic mechanisms. It is derived from the primary claim of ID which, according to Meyer, is that information, in particular complex specified information, can only be generated by an intelligent agent. It follows that undirected processes, or those without the influence of an intelligent agent, cannot generate such information. Meyer selected the target of 500 bits to roughly correspond to Dembski’s assessment of the threshold of the universal probability bound. Fewer bits could conceivably arise by chance given the full resources of the universe.

“This prediction can be clearly falsified by the discovery of an undirected physical or chemical process that can generate over 500 bits of functionally specified information,” says Meyer. While at first glance this seems to be a very straightforward and simple test, closer examination reveals it won’t be that easy. There is no specification for the specified information so it isn’t clear just what kind of information is required. Does this refer to full-scale DNA information with all nucleotides and related biomolecules in place to show that replication takes place? Or would a simpler set of information suffice? We’ll assume the former. If one were to believe that life originated through naturalistic processes, it probably took a hundred million years to occur. In order for the process to be demonstrated in our lifetime, it would have to be accelerated by eight orders of magnitude. Such acceleration would have to be accomplished by human intelligence and would undoubtedly be interpreted as a violation of the “undirected” aspect. It may therefore be impossible to falsify.

Fulfillment of the prediction is also likely to be inconclusive. Predictions of null results are notoriously problematic since too many alternative factors can lead to null results. Most origin of life projects are aimed at understanding specific chemical reactions that might be a part of the big picture. Few, if any, are focused on a full-scale demonstration of the type predicted. A null result need not be due to the impossibility of materialistic processes.

In summary, it seems very likely that this prediction will be fulfilled but that it will not persuade anyone that it is due to the validity of ID.

He writes that “intelligent-design hypotheses may generate several distinct types of predictions: predictions about causal powers, or lack thereof, of various mechanisms; predictions about the structure, organization, and functional logic of living systems; predictions about what evidence will show about the history of life; and predictions about the causes of putatively bad design.”

Meyer proposes a dozen predictions that he believes render ID testable. Each of these predictions will be discussed in a post in this blog. Subsequently, we will consider the arguments presented in chapter 18.

Meyer carefully distinguishes himself from Thaxton by going beyond him in the degree of confidence with which a scientific endeavor can determine historical causal events. Thaxton expressed doubts that origin science can lead to conclusions that are as compelling as observational science: “Theories about the past can produce plausible, but never decisive conclusions.” (p.30) Meyer believes that there are methods that make studies of historical causality more than just plausible, or possibly true. His quest led him to a study of Lyell, Darwin, and Scriven, as discussed in a previous post. Meyer makes a credible argument that it is possible to reliably determine the cause of events in the past, when sufficient data are available. This conclusion is essential to his ultimate goal, which is to show that an indeterminate intelligent designer was the source of the origin of DNA information. If the historical sciences were not reliable, such a conclusion would have little weight.

The reaffirmation of the reliability of conclusions drawn by the historical sciences, even in the case of intelligent causes such as hominids, is an important step. Too often in our society the historical sciences are considered to be distinct from operation sciences in the degree of confidence one can have in their conclusion. There is, of course, a difference in terms of repeatability. In operations science, a theory describing a repeating phenomenon can be tested by its predictions of future occurrences. Historical sciences, on the other hand, deal with unique events and such theories cannot be tested by repeating an event. But, as Meyer goes on to point out, historical causal adequacy can nevertheless be tested when there is independent evidence of causal existence, causal adequacy, and causal uniqueness. That is to say, that even though the same event cannot be repeated, there can be tests of comparable causal factors that can lead to decisive conclusions of the cause of the original event.

By affirming the credibility of the historical sciences, Meyer effectively distinguishes ID from creationism. Creation science proceeds from the premise that historical science is not credible. The primary reason is their presupposition that God changed the laws of nature in the past, rendering extrapolation of those laws impossible. For instance, starting with a belief that the earth is less than 10,000 years old, the only reconciliation with observations of the distance of stars or of the magnitude of past radioactivity, is to assert that the speed of light or the radioactive decay constants have changed in the past. Hence, it is not possible to reliably study the past, unless one were there to document the laws of nature at that time. Alternative explanations of past events can be derived by using physical “constants” as variables that can be adjusted to fit data. Such a view would undermine the credibility of a conclusion that an indeterminate intelligent designer was the causal factor of the origin of life.

Meyer points out that one critic had called ID creationism in a cheap tuxedo. By affirming the validity of historical sciences, Meyer shows that the difference between ID and creationism is much more profound than that.

The magnificent example of antibodies was presented by Dr. Craig Story in the December 2009 issue of Perspectives on Science and Christian Faith, Vol. 61, No. 4, p.221. (if you aren’t a member or don’t have a subscription, copies are available from the ASA office for $10 plus shipping and handling; contact asa@asa3.org.) In his article, Craig explains how the immune system works, focusing on the importance of the inherent randomness in the process. In this post, I would like to offer a physicist’s interpretation of his paper, with a focus on the information content. Craig has graciously reviewed these comments and corrected my errors in biology.

Stem cells in our bone marrow continuously produce a population of pre-B cells, so called because they are precursors to B cells, which manufacture antibodies when mature. These pre-B cells are all identical and have the same antibody gene DNA. This population therefore has a relatively low information content. All the complexity is within the cell and there is no diversity in the population of cells. As the pre-B cell population prepares to moves into the body, the cells undergo a transition into B cells. In the process, key segments of DNA in each cell are rearranged randomly to form a unique and novel DNA sequence. The process is described in detail in Craig’s paper. It is a constrained process so that the resulting antibody protein is always a particular folded configuration that may have affinity to an antigen, but the gene segments are randomly rearranged and joined to alter the magnitude of the affinity. The result is a population of B cells, each one of which is different in terms of its antibody DNA. This means that we have a transformation of a low information population of pre-B cells to a high information population of B cells, with reference to their antigen-binding abilities. The complexity has increased dramatically but we do not yet have specificity.

As a B cell moves through the body, it may or may not encounter an antigen with which it has affinity. If it does not, the B cell dies and that particular configuration no longer exists in the body. However, if an antigen appears with which a B cell has some degree of affinity, the B cell will attach to the antigen. In this case, that B cell will reproduce through cell division to create clones of itself. This process occurs throughout the population of B cells with the result that only B cells with some affinity to the environment of antigens survive. This is a basic level of specificity.

There is another level of specificity that Craig describes. A first-responder B cell usually will have a relatively small degree of affinity to an antigen. As this cell reproduces itself, an enzyme enhances the mutation rate of only the portion of the antibody genes that determines the affinity. In some cases, mutation rates can reach as much as one nucleotide per cell division. This means that the subpopulation of this particular B cell grows with a dynamic diversity of various degrees of affinity to that antigen. The cells with the strongest degree of affinity will preferentially attach to the antigens, leaving those with weaker affinity without antigens and therefore a death sentence. Over time, this subpopulation will be predominantly one with strong affinity to this particular antigen. This, in a nutshell, is why vaccines work.

In the bigger picture, this example shows how a homogeneous population of pre-B cells is transformed to a dynamically diverse population of B cells, with a tremendous increase in information content. This complex information then becomes highly specified by fine-tuning to match the antigens to which they are presented. The result is a high degree of specificity and complexity with no involvement of an intelligent designer as an immediate cause. This does not, of course, preclude the sustaining involvement of an Intelligent Designer at a metaphysical level.

Craig points out the critical role of randomness as a key characteristic of the cellular processes involved in the immune system. The random process of gene rearrangement is necessary to ensure a sufficiently broad range of binding specificities, such that some of them are almost sure to bind to one part of each pathogen. His example also illustrates clearly how highly complex and highly specified information is derived directly from a population of relatively low-information cells. Hence, the argument that Meyer makes that all complex specified information comes from an intelligent source does not withstand scrutiny.

The antibody example is a beautiful illustration of the basic processes of evolution. It begins with the common ancestry of the stem cells that produce an ancestral population of pre-B cells that are essentially identical. Descent with random variability occurs in the generation of the B cells, which are all unique with respect to their antibody gene DNA. Natural selection describes the way in which B cells that do not bind to an antigen will die while those that do bind to an antigen proceed to reproduce clones. The random variability of the dynamically diverse population of antibodies ensures the formation, within a short period of time, of antibodies with affinity to virtually any antigen. The subsequent way in which those B cells acquire stronger affinity to that antigen is a type of adaptation. Darwin suggested that these basic processes, operating over a long period of time, could account for the origin of species. Little did he suspect that these very processes are active continuously in our bodies on a relatively short time scale to provide a vital line of immunological defense.

There is no doubt that there are many intriguing similarities between DNA information and computer code. Many of the same analytical tools can even be used in both systems. But Meyer says that this is not the basis for the inference to an intelligent designer. Rather, it is the fact that both are identical insofar as being specified information. I do not find his argument compelling because the two systems derive their specificity from two different sources, as Meyer himself admits, though he does not pursue the consequences of that difference. DNA information is specified because it works. The cell is able to carry out a very complex function. Cells that do not function, die. Those that function, live and undergo cell division. Computer code is also specified because it works, but now its function is defined by the meaning that intelligent beings have assigned to the “0”s and “1”s that are generated by the computer. The computer system is rife with abstraction and it functions because its physical complexity conforms to the basic principles of computer design and because of the abstract meaning assigned to those physical states. This difference is crucial and stands in contrast to the claim of identicality.

The physical requirements for a computer are simple but not trivial. Rolf Landauer described the requirements for physical states in an information processing system in a paper in 1961 (R. Landauer, “Irreversibility and Heat Generation in the Computing Process” IBM J. Res. Develop. Vol. 5, No. 3, 1961). Essentially, a stable physical binary state must exist which can be switched from one state to another. These states can be designated as “0” or “1” but that designation is independent of the specific physical system being used. The second requirement is a channel to transmit that information. Claude Shannon was the pioneer who quantified the information that can be transmitted in a physical system (C.E. Shannon, “A Mathematical Theory of Communication”, Bell System Technical Journal, vol. 27, pp. 379-423, 623-656, July, October, 1948). The third requirement is a set of logic gates that carry out Boolean logic, first defined by George Boole in the 19th century. These are the basic principles of computers. In addition there are numerous constraints to make a computer practical, fast, and efficient but those need not concern us here.

The key point for our purpose is that the functional specificity of any computer designed on the above principles depends on abstraction. There are many levels of abstraction. The first and most basic is assigning “0” and “1” to a binary system. This assignment is independent of the physical system selected and the “0” and “1” can be interchangeable, as long as it is done consistently. For example, a positive voltage may be designated a “1” and a zero or negative voltage may be a “0”. But there is nothing about the voltage that demands that it be a “1” or a “0”. The definitions can be reversed. Or a polarized photon could be used as a “0” or a “1”. It could be polarized either linearly or circularly. The “1” could be horizontally or vertically polarized and the other would be “0”. This assignment is arbitrary. Higher levels of abstraction are assigned to interpret binary strings in terms of ASCII codes or instruction sets or data or various other meanings. But the chemistry and physics of the system is irrelevant, provided the basic constraints are met.

In contrast, DNA information is composed of a linear set of units called nucleotides. Each sequential step of DNA has one of four nucleotides, usually designated A, C, T, or G for short. The sequence is critical and is the essence of the information, but its function also depends on a large number of supporting biomolecules, most of which are, in turn, generated from various segments of the DNA. Thus far, the similarity to computer code seems strong. But as we look closer, we see that the functional specificity is derived precisely from its ability to survive and reproduce, not from any abstract meaning. If the chemical and physical structure changes, the functionality is changed and may be lost. The information is not a matter of assigning a meaning to the nucleotides, but rather the chemical function that the DNA carries out. This is a crucial distinction because it is abstraction itself which is the strongest (though not the only) indicator of the action of an intelligent designer.

To summarize, the ID community, and specifically Meyer in this book, often cite the comparison of computer code and DNA information as evidence of an intelligent source for both. But though they both have physical complexity that we call information, there is a critical distinction in that computer code specificity is derived from abstraction of the physical complexity whereas DNA specificity is derived from its chemical ability to survive and reproduce the physical complexity. That fundamental distinction undermines the core argument that Meyer seeks to make. Specificity of complex information does not arise uniquely from an intelligent source but also from physical or chemical functionality.

I’ll try to summarize the key points here once more.
Information is physical complexity which may occur through natural causes or crafted by intelligent agents. How can one determine when information is generated by an intelligent agent? Meyer focuses his book on the ID claim that when information is complex and specified, it necessarily comes from an intelligent source. Information, he states, can be specified either by being “functional” or by having “meaning.” He shows that DNA information is of the “functional” type.
The question is raised here whether Meyer has adequately demonstrated the claim that both “functional” and “meaning” type of information must come from intelligence. I suggest that only the “meaning” part has that characteristic and not the purely “functional.” The rationale is that “meaning” is the abstract significance applied to a physical configuration, a feat characteristic of intelligence. All the examples Meyer brings forth to demonstrate how information comes from an intelligent source involve “meaning” or a mixture of “meaning” and “function.” Functional configurations can occur in nature that are quite complex and need not have an intelligent source. This leaves open the question of whether DNA information was derived from an intelligent source.

The second claim examined in the lecture deals with the post on historical causal analysis. To be scientific, a claim for having detected a designer for some event, must involve a designer whose existence and means of design must be independently observable. An indeterminate designer does not meet that criterion.

Point #1: Meyer devotes his book to showing that CSI always comes from an intelligent source and since DNA information is CSI, then DNA information can only come from an intelligent source. He also shows that there are two types of CSI, “function” and “meaning”, and that DNA information is of the function type. My question and concern is that Meyer has not provided convincing evidence that CSI always comes from an intelligent source. Two problems come to mind. Firstly, his argument is inductive in nature, claiming that all examples of CSI are from intelligent sources. Unfortunately, it is not a compelling conclusion that CSI must therefore always come from an intelligent source. Secondly, all of his examples of CSI coming from intelligent sources are of the “meaning” type, and there is no reason to expect that information of the “function” type must have the same source.

Point #2: I also suggest that rather than using CSI as an indicator of information with an intelligent source, Meyer’s categories of “function” and “meaning” may be more appropriate and useful, if defined more precisely. The former type may or may not have an intelligent source while the latter always does, even if it isn’t complex or specified. How can “function” and “meaning” be distinguished? All information is physical and is embodied in physical complexity. In “function” type of information, the information has a necessary relationship to the physical complexity that conveys it, whereas in “meaning” type, the information has a contingent relationship. That contingency is based on abstract reasoning and is a clear indicator of intelligence.

Several examples may be useful to help understand these concepts. First of all, let us consider a simple coin toss. As stated in a previous post, coins with smooth, indistinguishable surfaces do not convey any information. The coins need some physical complexity. But that complexity does not have a necessary relationship to the designation of “heads” or “tails.” It is a contingent relationship meaning that the H or T designation is not required by the specific physical configuration. Even a picture of a head could be designated “tails.” Clearly, this is an example of “meaning” type of information and an intelligent source is required to generate it.

A second example is language. The first letter of the English alphabet is recognizable from its distinctive shape. But there is nothing in that shape that requires it to be designated as the first letter of the alphabet. Such designation is contingent and is based on abstract reasoning. In language there are many levels of meaning, from alphabet to word vocabulary to sentence structure. Intelligent sources provide that abstract meaning.

Another clear example is computer code and, in general, all digital information technology. All information is stored, processed, or transmitted as the binary digits, or bits, “0” or “1”. The physical configuration may be a voltage level, or a charge on a capacitor, or a polarization state of a magnetic domain. None of these physical states has a necessary relationship to either a “0” or a “1”. The assignment is arbitrary and, as long as the system is consistent, doesn’t affect the outcome. Therefore, all digital information is of the “meaning” type, at many different levels, and at the most fundamental level.

Numbers are also of the “meaning” type. Meyer uses several examples of telephone numbers to illustrate CSI. However, numbers can indicate an intelligent source even if they are not complex or specified. The number “3” for example conveys meaning of the number of objects. The physical configuration of the numeral does not have a necessary relationship to the number of objects. It is contingent and comes from an intelligent source. Imagine, on the other hand, that in a forest with no intelligent beings, three twigs fall from a tree and lie side by side on the ground. Their presence indicates three objects as a necessary and not a contingent consequence. It cannot be easily determined whether an intelligent agent had deliberately arranged the twigs to indicate “three” or whether they fell from the tree. Abstraction is not evident.

DNA information is of the “function” type, as Meyer points out. Close examination shows that this information has a necessary relationship to the chemical structure of the physical configuration expressing the information. The information conveyed and expressed at any step of the activity of a living cell is the chemical reactivity itself, not a contingent meaning assigned to it. Though many complex and specific functions are exhibited, there are no examples of abstraction in the operation of a living cell. Perhaps an intelligent agent is involved at some level but it cannot be determined from the nature of DNA information.

In summary, abstraction seems to be a useful indicator of whether or not an intelligent agent is required for the generation of information. Physical complexity is always a necessary part of information but that complexity may or may not be generated by an intelligent agent and it may or may not have meaning associated with it. Where we find evidence of abstraction, we can be confident of an intelligent source. One way to detect that abstraction is to examine the relationship between information and the physical complexity that embodies it. Contingence indicates abstraction while necessity does not. All the examples that Meyer provides to show that CSI comes from an intelligent source show contingence. DNA information does not. It appears that DNA information does not necessarily come from an intelligent agent.

Consider tossing four coins. The resulting sequence of heads and tails would be one of 16 possibilities. The amount of information is proportional to the base 2 logarithm of the number of possibilities, or 4 bits. That can be more easily determined by counting the number of coins, but in the more general case the mathematical form is more useful.

Imagine taking these four coins and grinding them perfectly smooth on both sides so that the two sides are indistinguishable. In this case, tossing the four coins leads to zero information since all possible outcomes are equivalent and there can be only one result. This illustrates how physical complexity is necessary to store or convey information. Smooth coins do not convey abstract information. There must be at least enough physical complexity so that the two sides of each coin are distinguishable in order to represent classic heads/tails information. Of course some information is still present in the particular shape of the coins. Simple functionality can be present, such as serving as a slug in a vending machine.

Next, consider modifying the smooth coins in any manner so that the two sides are distinguishable. The assignment of “heads” or “tails”, or if one prefers “0” or “1”, is a process of abstraction. The label “heads” has no necessary relationship to the physical feature that distinguishes that side of the coin. The ability to make such an abstract assignment is a unique characteristic of intelligence. For this reason, information characterized by abstraction is a good indicator of an intelligent source. On the other hand, the presence of complexity, per se, need not require any intelligent agent.

Finally, we consider another case whereby the two sides of the coins are distinguished by its chemical properties rather than by any other feature. For example, one side could be copper and the other side gold. Tossing these coins on an oxidizing surface would result in a pattern of chemical reactivity that conveys complexity. No abstraction is involved but there is chemical functionality. Technically, the amount of information is the same in both cases, but in one case there is abstraction, an indicator of intelligence, while in the other there is chemical functionality, but no evidence of intelligence.

Four coins are too simple a system to meet the complexity requirement of CSI but the principle scales up. Abstraction at any degree of simplicity or complexity indicates an intelligent source. Functionality alone, with no abstraction, may or may not involve an intelligent source at any degree of complexity.

In summary, information is inherently associated with physical complexity. That complexity can be associated with significance as assigned by an intelligent agent through a process of abstract reasoning. Alternatively, that complexity can be associated with a type of functionality that depends on the chemical and physical properties of the system. It can also be a mixture of abstraction and physical properties. The unique indicator of an intelligent source of information is abstraction. For DNA, no such abstraction is evident. This does not mean that an intelligent agent is not involved, it just means that the existence of DNA information does not require its source to be an intelligent agent.