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Reactive or creative evolution?

Calculating the phenotype from the genotype

Calculating the power of evolution

Prediction in biology versus physics

Definition of life


Contents Table of Contents

Further Reading

different page Reply by the author Mark Ludwig

different page Similarities and Dissimilarities of Computer and Biological Viruses

Mark A. Ludwig as an early Intelligent Design Advocate

A review by Gert Korthof, 2 Feb 2006 ( updated 9 March 2024 )

Computer Viruses, Artificial Life and Evolution       Mark Ludwig's 'Computer Viruses, Artificial Life and Evolution' starts with a scientific study of computer viruses and Artificial Life, transforms into a defence of Intelligent Design and ends with a confession of belief in the supernatural.

      Summary of the book:
Computer viruses are autonomous self-reproducing agents. They infect other computers just like viruses and parasites in the biological world do. Computer viruses are a form of artificial life. Mark Ludwig claims computer viruses are the only artificial life forms in the wild of which one can claim that their environments (operating systems) are not specially constructed to let them work. Therefore, naturally occurring as well as specially constructed computer viruses are the ideal objects to study the feasibility of computer virus evolution without philosophical or religious bias. Above that, they are easier to study and with more exact methods than real life. He finds that computer viruses can exhibit Darwinian evolution.
He uses the results to evaluate real-life biological evolution and especially neo-Darwinism. Experimental and theoretical evolutionary biology are in a abysmal state because they are not predictive. The problem of the neo-Darwinian theory of evolution and the claims of the Artificial Life community is that both ignore the role of intelligence and information injection. Darwinian evolution is not a creative process: it does not create information; it only reacts to environmental selection, which is the origin of the information. So evolution is only a reactive force. Ludwig suspects that the environment (laws of nature) is engineered to make life and evolution possible. Ludwig is also interested in the origin of life question.
He organised a contest to write the smallest possible functional self-replicating computer virus and concluded there is an absolute minimum size. He concludes from the minimum size that random processes inside a computer cannot create the smallest virus; even then such a virus could be an evolutionary dead end. All viruses we know about cannot arise by random processes, so must be intelligently designed by people. The same improbabilities apply to the spontaneous origin of a self-replicating molecule capable of progressive evolution.

       Reactive or creative evolution?  

      In the world of computer viruses the hypothesis of intelligent design is unproblematic because we know that humans create computer viruses (Ludwig himself included), but in the biological world things are different. What argument does Ludwig advance for ascribing intelligent design to evolving organisms? Ludwig's argument heavily relies on the distinction between 'reactive' and 'creative' evolution (7). 'Reactive' means reacting on the selection pressures of the environment. Darwinian mutation and selection do not create information, because "the selection process is injecting information into the system", just like Richard Dawkins injects information in his artificial selection experiments. Creative evolution does not exist according to Ludwig (2).

      Does this make sense? Ludwig overlooks the fact that natural selection (the core of Darwin's theory) means the interaction of the environment with the organism. The environment can be the physical environment. Consider for example aerodynamics. Do the laws of aerodynamics inject information into the genomes of birds, bats, and insects? In an abstract sense, yes. Consider an extraterrestrial civilization analysing the anatomy of a bird, bat, and insect. They clearly could infer certain properties about our earth's atmosphere and gravity without investigating our planet itself. So there is information about the environment built into these organisms. Next consider the different anatomical solutions of birds, bats and insects. It is hard to see how these different solutions could follow directly from the laws of aerodynamics. The different solutions for the same problem arose from heritable variation, natural selection and common descent. Information injection? Yes, but it has arrived there in a natural way. No miracles here.

      Secondly, the environment of an organism often is another organism. More than half the world's species live in or on the bodies of other organisms. Carnivores and herbivores depend on other organisms. Prey-predator are in an evolutionary arms race. In insects (butterflies) mimicry is the result. Co-evolution of organisms means a two-way interaction. In the computer world: viruses and virus scanners co-evolve. Even in the virtual computer world viruses contain features of their environment because viruses are designed with full knowledge of the target Operating System (OS). So although Ludwig is right that the OS is not designed for viruses, the reverse is true. No wonder that viruses work. So, do organisms 'inject information' into each other? Again, yes, in an abstract sense and again by perfect natural Darwinian processes.

      Ludwig's label 'reactive' is not an objection to evolution by natural selection at all. Darwin never claimed that evolution created species 'out of thin air'. The creative aspect of evolution is that species change by natural selection and thereby change the information content of their genomes. This change of information content is natural and not supernatural. There is no philosophical materialism necessary here. Since Ludwig accepts reactive evolution and his distinction between reactive and creative evolution melts away, his argument against creative evolution evaporates too.

Does information increase during development?

Does information increase during development? If it does, then development apparently is a creative process. Let us define the algorithmic information content (16) of a fertilised egg of an animal as the shortest full description of the egg on the molecular level. It is clear that even for an egg the sequence of DNA in the egg is not enough for a full description (information in cytoplasm). When the egg grows into an embryo, foetus, baby, child, and adult the algorithmic information content steadily increases. The reasons are manifold. Size alone means there are more molecules to describe. Events during life, such as virus infections change the immune system based on a creative genetic process (more). Mutations in body cells cause cancer and increase the total information content of the body. Whether or not this increase should be described 'reactive' or 'creative', nobody would claim that supernatural intervention is necessary to explain information increase during the development and life of an individual. Why would evolution need supernatural causes to explain information increase?

       Calculating the phenotype from the genotype  

Rene Magritte Clairvoyance
Rene Magritte calculating bird (phenotype) from egg (genotype)

The first half of the book is based on the idea that viruses and Artificial Life are nothing but instructions to be executed (by a computer). Ludwig translates this view to biological systems. By analogy the genotypes (DNA) of living organisms are also viewed as instructions to be executed (by a cell). The name of the game is now: calculate the phenotype from the genotype. This seems reasonable if the genotype is available and can be computed (by a computer) and those instructions fully determine the phenotype. Let's first discuss the computation of the phenotype, then the computation of the power of evolution (Power).

The essential questions of evolution
The essential questions of evolution (page 155)
Fitness function unfriendly to evolution

Fig. 20.10. Fitness function unfriendly to evolution (page 215)

Whatever the computability of Artificial Life and Evolution, biological research is not done and cannot be done by computing complete phenotypes from genotypes (with or without interaction with the environment). Richard Dawkins (2009) wrote:
"Nobody, reading the sequence of letters in the DNA of a fertilized egg, could predict the shape of the animal it is going to grow into. The only way to discover that is to grow the egg, in the natural way, and see what it turns into. No electronic computer computer could work it out, unless it it was programmed to simulate the natural biological process itself, in which case you might as well dispense with the electronic version and use the developing embryo as its own computer." (28).
"Even if I knew the complete molecular specification of every gene in an organism, I could not predict what that organism would be."
Lewontin, 1992, 25-26, quoted in Depew and Weber Darwinism Evolving, 1995, p.378.

"Despite these breakthroughs, we still have little idea of how many genes, when disrupted, will produce phenotypic changes." (34)
So, Ludwig is on the wrong track. His drawing (page 155) of the relation between genotype, phenotype and environment is extremely simplistic. Remarkably, in a sense Ludwig knows that one cannot compute a complete phenotype (15). He claims that the phenotype is 'emergent' because it cannot be calculated from the genotype. This is important for him: emergent behaviour is part of the definition of life he uses (see: Definition).

      One cannot even begin to ask the question how a genotype produces a phenotype, if one does not know the genotype. Biologists rarely know the complete genotype (genome) of a species, although knowledge is rapidly growing. Before DNA sequencing (genomics) was discovered, a (partial) genotype could only be indirectly inferred from the phenotype. Gregor Mendel was the first who inferred genotypes. Today, more than 1,800 genes are known to cause hereditary disorders in humans (Online Mendelian Inheritance in Man). The first direct access to the genotype was possible through the study of aberrant chromosomes (cytogenetics). For example, prenatal diagnosis can predict the Down's syndrome phenotype from the trisomy-21 genotype with nearly 100% certainty. A hundred years after the rediscovery of Mendel in 1900, DNA sequencing techniques opened for the first time direct access to complete genotypes. Biologists and biochemists needed a hundred years to arrive at a point where AL-scientists simply started: knowlegde of complete genotypes. As if biologists simply needed to load the genotype into a computer and run it!
Only in 2012 computational biologists were able to do just that for the smallest organism known to us: the single celled Mycoplasma genitalium with 'only' 525 genes. They developed a computer model with 1,900 experimentally derived cellular parameters. The model is able to predict phenotypes (biological properties, cell behaviour) from genotypes (gene sequences) (31). This is an impressive achievement, but it is far from predicting the phenotype of a multicellular organism.

The difference between biology and AL is that:
  1. AL always knows the complete genotype simply because the genotype is the software they have written themselves;
  2. in AL it is always possible to compute whole phenotypes from the genotype simply because the program has been written to do just that : produce a complete self-reproducing agent. Half an organism does not reproduce very well.
  3. biology cannot predict a complete phenotype from a genotype, because it is impossible to calculate the net effect of thousands of genes of multicellular organisms (humans have 22,500 genes), the mutual interactions of their cells and proteins and interactions with the environment (24), but see (31). Predictions are certainly not yet possible for multicellular organisms which consist of thousands up to billions of cells (22) which differ in the parts of their genomes that are being read or not read. There does not exist a physical system so complex as a multicellular organism.

Not predictable in strict sense

One of the reasons for the incomputability of real biological organisms is unpredictability (in the strict sense) at the molecular level. In 1953 Watson & Crick discovered the structure of DNA. On the basis of its structure alone it is impossible to predict how the bases in DNA are translated into the amino acids of proteins. Mathematics did not and could not solve the genetic code problem. The answer cannot be computed. Only after more than 10 years the relation between base sequence and amino acid sequence could be established empirically in the laboratory. The decoding of the genetic code made it possible to predict protein sequence from an arbitrary gene sequence with high accuracy (a few exceptions occur, which again could only be discovered in the laboratory).
That is only the first step from genotype to phenotype. "While it is a challenging problem to predict the structure of a protein form its amino acid sequence, it would be impossible to tell what a protein did in a cell" (my emphasis) (3). Similarly, it seems nearly impossible to predict what a particular gene is for, based solely on its base sequence. Moreover, it is impossible to predict with certainty the number of genes encode in a genome on the basis of the complete DNA sequence.
Another matter is predicting when and where a gene will be expressed. This can only be established experimentally, but cannot be predicted. It can be experimentally established which regulators regulate the expression of a specific gene, but again this cannot be predicted from the DNA sequence of the gene alone. Additionally, it cannot be predicted which amino acids of the protein are essential for its function, but knowledge of protein variation in populations in the wild helps a lot.

Not by genes alone

Do genes code the organismal form? Not quite, says evolutionary biologist Massimo Pigliucci. "Genes by themselves do literally nothing. Organisms do not begin with a bunch of genes that generate everything else: they need a set of environmental conditions, as well as the inheritance of materials and extra-genetic information from the previous generation. From the point of view of causal analysis, genes may be said to be a necessary but far from sufficient condition for the development (and evolution) of organisms." Clearly, this is another reason why the phenotype cannot be computed from the genotype.
Original link: http://life.bio.sunysb.edu/ ... does not exist anymore.

To construct a complete 'fitness landscape' one needs all the data of the phenotype effects of all possible protein variants of the whole organism. So it is clear that a accurate fitness landscape cannot be calculated. In practice geneticists isolate and study well defined phenotypic effects of well defined mutations. This works fine. In the same way evolutionary biologists (population geneticists) calculate what will happen with well defined mutations in a population. This works fine too.
It is easy to construct a hypothetical Fitness Function unfriendly to evolution as Ludwig did. To construct a realistic fitness landscape one needs a lot of data. Ludwig's figure (page 215) is just an imaginary illustration. It is not based on data. No conclusions about Darwinian selection can be based on that.

       Calculating the 'Power of Evolution'  

Ludwig asks the BIG question:"Are the mechanisms proposed by biologists powerful enough to produce all life on Earth?". Ludwig observes that "most biologists believe evolution is powerful enough to create all the complexity and diversity of life we see on earth over the period of about a billion years." (4). Then he concludes:"we need a scientific theory of the power of evolution.", "Today, we don't even have a theory" (p.153), "nobody can really prove it is powerful enough to do the job." (4). A different question is: Is evolution not too powerful for the job? Is evolution infinitely powerful like God? (14).
Ludwig's expectations for a theory of evolution are strongly influenced by the field of Artificial Life. Evolutionary processes run on a computer, so are fully computable. In analogy with Artificial Life, Ludwig thinks an evolutionary theory in biology must compute the generation of complex life. "A theory, though, ought to give me the tools to start with a set of initial conditions and predict what is going to happen." (4). Then he concludes that evolutionary theory fails in making these kind of predictions and" Experimental and theoretical evolutionary biology are in a abysmal state"! (4).

      Requirements for a theory of evolution

Are Ludwig's requirements for a theory of evolution reasonable? Not really. Ludwig knows that real biological organisms are too complex to be modelled on a computer, because the phenotype cannot be calculated from the genotype (Ludwig's 'emergence' is the defining property of life!), let alone that evolution of a population of those organisms can be predicted. It does not make sense to blame biologists for this when complete phenotypes are fundamentally unpredictable. There are several good reasons for the difficulty of calculating the power of natural selection. One reason is that species have histories. That makes them unique. "Evolutionary biology is a historical science" (10). Additionally, a complicating factor is the role of chance in evolution: "The outcome of an evolutionary process is usually the result of an interaction of numerous incidental factors." (10). Despite this, some evolutionary biologists claimed things as "The All-Sufficiency of Natural Selection" (11). But even if the majority of biologists would claim something like the above, the answer still depends on the precise meaning of the claim. What precisely has to be explained? And in what detail? Is the theory of evolution required to predict all the details of all species that ever lived on Earth (the existence of the duck-billed platypus, giraffe, flying fish, panda's thumb, chromosome number of humans, blind spot in human eye, introns in genes, etc) in contrast to general principles (adaptation, the power of natural selection, mimicry, parasitism, sex, sexual selection)? According to historian Roger G. Newton, the thing that has gradually separated physics from the other branches of science over the past 6,000 years is "the ability to predict future events with some confidence of success." (26).

      Prediction in biology versus physics

Solar system. Do the combined laws of physics and cosmology explain all the details of the non-living universe? Newton's laws of gravity predict with such precision which orbits planets can have, that eclipses of the sun and moon can be made with amazing accuracy and they are routinely successful. However, the absolute distance between the Earth and the Sun, 92,955,807 miles, could not be derived from Newton's theory. Nearly a hundred years after the Principia that number has been obtained by experiment and observation (49).
A possible explanation for the predictive power is that "Newtonian physics is essentially timeless" (33). In biology the direction of time is essential. The perfect predictability of Newton's laws might be undermined simply by the presence of too many mutually interacting bodies. For even just three bodies (40) following Newton's laws, vanishingly small differences in the initial conditions can lead to widely different outcomes over long times – giving an appearance of randomness even though the process is in principle entirely predictable. This kind of 'deterministic chaos' is now known to be present in the orbits of planets in the Solar System (38).

The 'timeless character' of the physical laws causes also problems. "The planets' orbits are chaotic over longer timescales. This means that the position of a planet along its orbit ultimately becomes impossible to predict with any certainty (so, for example, the timing of winter and summer become uncertain), but in some cases the orbits themselves may change dramatically" (35, 36).
Ergodic theory studies systems that evolve in time, eventually exploring almost all their possible configurations (48). These systems are typically chaotic, meaning that their future behaviour can only be guessed using probability. So, better predictions in physics?

The details of the planets. All eight planets in the Solar System orbit the Sun in the direction that the Sun is rotating. Six of the planets also rotate about their axis in this same direction. The exceptions–the planets with retrograde rotation–are Venus and Uranus. Why? Above that, Uranus is the only planet whose equator is nearly at a right angle to its orbit, with a tilt of 97.77 degrees, possibly the result of a collision with an Earth-sized object long ago (45). Has this collision been predicted?
Apart from the long-term predictability of the solar system, Newton's (and Laplace's) laws cannot predict number, size, and distances of the planets of our own solar system. And in which direction around the sun must the planets go? (clockwise? anti-clockwise?) Neither do they predict which planets have moons or rings, there sizes and distances.
  • For example, can the origin of our own moon be explained? Its size and composition? Why only one moon? Why a moon at all? Why is the orbit of the moon around the earth elliptical? Why does the Moon show 59% of her surface to Earth? (source).
  • Why does Saturn have 82 moons and why rings? Why do some moons of Saturn orbit in a direction opposite to the planet's rotation?
  • Why has Jupiter >95 moons? (some are believed to be asteroids).
  • Do the laws of Newton predict why Mars has only 10 percent of the mass of the Earth? (23).
  • Do the laws of Newton predict Mercury's makeup (mostly iron core, with a thin veneer of rock)? Does Newton predict that Mercury orbits ythe sun in 88 days? That Mercury doesn't have moons and rings?
  • Do the laws of Newton predict Uranus's skewed magnetic field (30)?
  • Do the laws of Newton predict facts about Pluto?
Planetary science is a historical science
All planets have a history. Planetary science partly is a historical science just like evolution. Our solar system has an origin. It has a past, present and future. The physicist Paul Davies stated "We know now that the arrangement of the planets is largely a historical accident" (27). Even the modern general theory of planet formation does not explain the detailed structure of the Solar System (29), (32),(39),(41),(42),(44).
Planets have a history. For example: Pluto is now famously frigid but a new study finds that it may have started off as a hot world that formed rapidly and violently. The research suggests the dwarf planet had an underground ocean since early on in its life (46).
Mars: there is a Geological history of Mars, it has a Natural History! Natural history used to be the investigation of animals, fungi, plants in their natural environment!
Finally, there is the unsolved problem of the existence of the hypothetical Planet Nine. Its gravitational effects could explain the unusual clustering of orbits for a group of extreme trans-Neptunian objects (eTNOs), bodies beyond Neptune that orbit the Sun.

biodiversity versus astrodiversity
There are millions of biological species. There are 200 billion trillion stars in the universe. There are 620,108 known Planetoids orbiting around our sun. Planetoids are minor planets. There are more stars in the universe than biological species on earth, but biological species have high internal complexity (DNA). What does astronomical theory predict about the 1,131,201 known astronomical objects in our solar system?

Habitability of planets:
We know that the earth is habitable. But could physics have predicted that Venus might have been habitable in the past? Venus May Have Supported Life Billions of Years Ago. Can physics predict that drastic climate shifts 700 million years ago made the planet's atmosphere incredibly dense and hot and made life impossible? (47).

Similarily, physics cannot predict the famous constellations (Ursa Major, Orion, Cassiopeia, etc). Since historical accidents are a big factor in evolution, it is even the more unreasonable to require detailed predictions of the theory of evolution. At the moment, physicists cannot rigorously deduce the structure of the helium atom from basic physics, a non-historical problem, let alone that of a living organism. Even more fundamentally, the standard model of physics depends on 19 numerical parameters. Their values are known from experiment, but the origin of the values is unknown.

"In theory, the Navier–Stokes equations, developed almost 200 years ago, describe the physics of fluids well. But these equations are devilishly hard to solve. So engineers and scientists usually come up with simplified theoretical models or resort to numerical simulations when they want to predict fluid flow. This approach has its limits: modelling turbulence bogs down even supercomputers." (43).

So, if physicists are unable to predict the behaviour of dead systems, how reasonable is it to expect biologists to predict the behaviour of living systems? Evolutionary biologists do not 'tacitly assume' that evolution is omnipotent and therefore a mathematical quantification is not necessary (p.154). They do not assume it at all, but because they know natural selection is not omnipotent (14).

      fact - path - causes

There is an important distinction absent in Ludwig's evaluation of the theory of evolution, which blocks any meaningful result. That is the well-known distinction fact - path - causes (12). The evidence for common descent ('The Fact of Evolution') is so strong that biologists do not constantly try to prove or disprove it, just like physicists are not constantly trying to prove or disprove the second law of thermodynamics.
The historical path of evolution (phylogeny) may never be completely known in all its details.
The causes (mechanisms) of evolution can and are being experimentally investigated. Biologists do not test whether Darwinian mechanisms could produce all life forms on earth, they investigate the relative importance of different mechanisms and search for new mechanisms. Ludwig completely misses this three-part division. I recommend reading reference 12, but there are many others.

      population genetics

Furthermore, Ludwig completely misses the theory of population genetics (13). Despite all the theoretical restrictions, population genetics is the most mathematical thing in evolutionary biology: it comes closest to calculating the power of evolution (18). Population genetics is the most fundamental body of theory in evolutionary biology. It is the proving ground for almost all ideas in evolutionary biology (8). The difference of population genetics and Artificial Life is that population genetics makes useful abstractions. Typically, population genetics calculates what happens with genes, not with individual organisms.

In the last 150 years evolutionists did more to elucidate the powers of nature to create life forms, then all theologians in 3000 years! Did theologians prove that God has the power to create the universe? How could such a proof be possible, since God by definition has the power to create the universe? He was invented to do just that. If he could not do it, then we needed to invent a God that did have the power.

       Definition of Life  

Although Ludwig is sceptical about the possibility to give a list of defining characteristics of life, ("We don't understand life well enough to give a set of hard rules to determine what is alive" and " The very concept cannot be put in terms accessible to science.") he works with the list used by Artificial Life researchers:
  1. the ability to reproduce and the method of reproduction
  2. emergent behaviour
    • "a system exhibits strong emergent behaviour when one or more aspects of its behaviour is theoretically incalculable"
  3. metabolism
  4. the ability to function under permutations of the environment and interact with the environment
  5. ability to evolve
This list is a mix of real life criteria (#3,#4) and potential life criteria (#1,#5) according to Gánti's life criteria (review). That explains his problems. Ludwig states that the approach of refusing to call something alive unless it can evolve is rather blind (I agree). He claims that evolution cannot be used as a dividing line between life and non-life (I agree), and evolution must be divorced from the definition of life, but he does so for the reason that evolution 'cannot be observed'. However, living individuals have the potential to reproduce (#1) and the potential to evolve (#5), but need not actually do it at the moment of observation. Every thing that reproduces, has heredity and hereditary variation, has also the ability to evolve. The same holds for reproduction, which is also a potential and cannot be observed immediately. A cell that is not dividing is not dead. That's why reproduction and evolution are potential life criteria. Above that, evolution is a property of populations of individuals, not of individuals. Remarkably, Ludwig claims that computer viruses can be designed to show Darwinian evolution (I agree). Because Ludwig does not make the important step of discriminating between the non-overlapping units of life and units of evolution, he is driven to the conclusion that from a mechanical perspective, it seems safe to say that computer viruses have a fairly strong claim to "life" (I disagree). In the Gánti definition neither biological nor computer viruses are alive. Viruses are parasitic objects without their own metabolism and that's why computer viruses are not a good model for non-parasitic cellular life. Regrettably, most of his book is based on the assumption that viruses are alive (computer viruses are a good model of biological viruses, but that's another matter; see this page).
    Furthermore, Ludwig does not distinguish between the properties 'self-reproduction' and 'information system' (DNA). This prevents him from making an information subsystem a primary life criterion, and self-reproduction a secondary criterion of life. This is important because an information system has also the function to inform metabolism (gene > enzyme > metabolic pathway). That's why an information system belongs to the primary characteristics of life. The metabolic function of DNA is most clearly present in the soma, while the genetic function of DNA is present in the germline.


Ludwig's goal was an unbiased, unprejudiced assessment of the theory of biological evolution (1). Did he succeed? His approach was to study the evolution of computer viruses and Artificial Life. This seems a sensible thing to do, especially if you are a physicist with good knowledge of computer viruses but without sufficient training in biology. Ludwig's knowledge of computer viruses is unique. He published several books on computer viruses. He demonstrated that computer viruses can and do evolve (Darwinian mutation engine). What he says about viruses and Artificial Life (AL) I trust to be largely correct. However, the success of his approach ultimately depends on whether the results say anything meaningful about biological evolution. How did he establish that his results are relevant for biological evolution? Surprisingly, he did not even attempt to answer that question. He did not realise that it was a crucial question for the success of his investigation. Yet, he found it appropriate to claim that 'AL holds the promise of a real theory of evolution' (my emphasis). This statement is wrong. The reason why the statement is wrong is that AL has abstracted everything that is crucial for the evolution of life on earth: having a body, getting and digesting food, urinate, respiration, metabolism, maintaining body temperature, adjusting blood sugar, blood pressure, diploidy, meiosis, getting a mate, getting pregnant, etc, and all complications that go with these things. Paradoxically, despite incorporating 'the essentials of life', whatever can be calculated in AL does have restricted value for biology. Above that, viruses are parasitic, so are not a good model for non-parasitic life. Remarkably, Ludwig's knows that biological objects are too complex and computer viruses and AL are too easy to study, but at the same time he beliefs that AL done properly could reveal insights about biological life forms. He did not resolve this dilemma: how to gain insight in complex life if your method eliminates complexity right from the start? I'm afraid there is simply no substitute for studying the messy, wet and dirty thing called 'life'. This does not mean that mathematics has no role to play. The secret is making the right abstractions. One of those magnificent and very useful abstractions is 'The Selfish Gene' (Richard Dawkins,1976), that is the idea of a replicator. Darwin's message can be translated in the language of today with one concept: replicators (25). If anyone could have understood the power of the idea of the replicator, it's Ludwig because viruses are selfish replicators.

      The second question is the assessment of the theory of evolution itself. Ludwig claimed that 'the theory of evolution is in an abysmal state'. This statement is wrong, naive, arrogant and insulting. Two main reasons are: lack of relevant knowledge and presence of bias in the technical sense.
- Ludwig is unaware of relevant biological knowledge. Yes, life on earth is extremely complex, but it turned out that life is not too complex to make predictions of partial phenotypes. Gregor Mendel is a magnificent example of the success of biological science in isolating specific characteristics among thousands of them and to predict the frequency of them in the next generation with mathematical precision (17). Mendelian genetics gave rise to population genetics and out of the marriage of population genetics with Darwinism the neo-Darwinian Synthesis was born (more).
Ludwig failed to investigate the structure of Evolution Theory (more) and the evidence (I don't know any critics of evolution who sufficiently know the theory!). Apart from missing population genetics, he missed the argument for common descent. That led him to the idea that in evolutionary biology only the mechanism counts and additionally, when one cannot calculate the evolution of bacterium to humans, the whole theory fails (6). Ludwig is pessimistic about what wet biology has produced. Regrettably, Ludwig has no idea what biological research has produced. It is not the theory of evolution, but his knowledge of biology and evolution that are 'in an abysmal state'. It would be easy to make fun about the state of physics and mathematics (21).
- What about bias? Was Ludwig unbiased in the gathering of the necessary information and his judgement? If not, did he compensate for the bias? Bias is present when judgement is unfair. Applying standards of theory construction common in the physical sciences to biology is unfair, because biology is different from the physical sciences. Is there religious bias too? In the first half of the book (studying viruses and AL) he is admirably unprejudiced, but then he introduces Phillip Johnson. Phillip Johnson is neither a biologist, nor an Artificial Life expert, but a lawyer. Is it professional for a physicist to consult a lawyer to gain insight in the field of evolutionary biology? Ludwig entered unfamiliar territory with a non-expert guide. Above that, introducing and recommending Phillip Johnson is introducing religious bias into his investigation (9). This is not the same as making his whole book worthless, but it is the opposite of what he wanted to do. Further, he did read biologist Michael Denton. Denton is a critic of evolution (19). Ludwig's list of 'Selected References' is extremely one-sided. That is easy to establish. Nearly all his references are critical of evolution or Darwinism. Did Ludwig compensate for this bias in any way? Insight into the uniqueness of biological knowledge could have counterbalanced his judgement, but that is absent in his book.

      A different question is whether Ludwig is an Intelligent Design Theorist (IDT); the question addressed in the title of this review. It is not meant to dismiss Ludwig, but it is a question of the history of ideas. His book pre-dates Michael Behe (1996) and William Dembski (1999) who made the ID concept popular. At the time Ludwig wrote his book, the word IDT was not common. As far as I know, Dembski and Behe do not refer to him. In his own summary of the book, Ludwig does not advertise himself as an IDT, nor does he mention that the book is about IDT. So, what evidence do I have for my claim that Ludwig is an early IDT? The signature of IDT is clear: belief that nature does not have the creative power to create species (conspicuous in Johnson: 7); information is the essence of life; information must be injected into the system from outside the system; anti-materialism; limitations of Evolution Theory; rejection old-style creationism; "science can never tell us whether life actually began as the result of a natural chemical process or a divine miracle" (p.145); evolution is (only) a theory and should be tested; belief in the supernatural ("I have to admit the supernatural into my worldview", page 330); endorsement of Phillip Johnson. For Ludwig ID is not a superficial idea: it deeply penetrates his thinking (20). I do think that this strongly influenced his ability to judge the current status of evolutionary theory. I do think that it substantially contributed to the outrageous claim that 'evolutionary biology is in a abysmal state'.

      Despite all my criticism, I did enjoy reading and reviewing Ludwig's book, because he asks the big questions. Only an outsider is able to ask this charming and naive question: "I want to try to find out how likely it is that natural law could cause what is observed in the fossil record and in today's world." (p.146). This is nothing less than 'A Theory of Everything' in Biology! It is easy to forget the big questions because professional science usually is about small, manageable but more fruitful questions (5).


Table of Contents Mark A. Ludwig (1993) 'Computer Viruses, Artificial Life and Evolution'.
American Eagle Publications, paperback 373 pages

1. Introduction
2. Are Viruses Alive?
Part I: The Mechanics of Life
3. Mechanical Properties of Life
4. Self-Reproduction
5. Emergent Behavior
6. Metabolism and Adaptability
7. Evolution
8. Conclusions
Part II: The Philosophy of Life
9. The Importance of Philosophy
10. Ancient philosphy and Modern Science
11. Emergent Behavior Revisited
12. Self-reproduction and Information
13. Autonomy
14. So Are Viruses Alive?
Part III: The Genesis and Evolution of Life
15. Introduction
16. The Creationist's Fall
17. Evolution, Myth, and Mathematics
18. The Creator and the Created
19. The Fact of Evolution
20. The Theory of Evolution
21. The Real World: Evolution
22. In The Beginning
23. The Real World: Beginnings
24. The Juggernaut of AL?
25. The New Evolution?
26. Last Words
Appendix A: Introduction to Cellular Automata
Appendix B: Some Basic Biochemistry
Appendix C: The First International Virus Writing Contest
Appendix D: Solving Differential Equations
Appendix E: Stochastic Population Equations
Appendix F: The Darwinian Genetic Mutation Engine

Selected References
A new edition Computer Viruses, Artificial Life And Evolution:
What Computer Viruses Can Teach Us About Life
was published by CreateSpace (February 9, 2009).

  1. Ludwig recognises philosophical and religious biases in the controversy about evolution: "There are people who outright reject evolution because they firmly believe God created the world. There are people who insist that evolution is a logical necessity because they firmly believe there is no God." (from his website).
  2. Remarkably, Ludwig accepts evolution in the computer world: "The reason is that evolution can proceed a billion times faster in the world of bits and bytes than it can in the world of carbon and water." (from his website).
  3. Jan Witkowski (2005) The Inside Story. DNA to RNA to Protein. Cold Spring Harbor Laboratory Press, page xv.
  4. Ludwig's homepage
  5. For example: the spread of a favourable gene in a population.
  6. The data supporting common descent are so strong that biologists do not test whether the mechanisms could produce the tree of life. But again, this is a big question.
  7. For Phillip Johnson the denial of the creative power of natural selection is crucial: "But what if Darwin was wrong, and natural selection doesn't have the fantastic creative power Darwinists credit it with?" (Darwin on Trial, page 111 and many other pages). Ludwig read, recommended and admired Johnson, so very likely he got the idea from Johnson. Historically the idea is older: Ernst Haeckel: selection only eliminates the less successful species. Bowler,2003, p.191.
  8. Mark Ridley (2004) Evolution. Third edition, page 93.
  9. "My primary goal in writing Darwin on Trial was to legitimate the assertion of a theistic worldview in the secular universities.", Phillip Johnson, 2nd ed page 165. I really don't understand why one wants to destruct Darwinism with any means in order to defend a theistic worldview. It is confusing two things. But that is another matter. See here what's wrong with Johnson.
  10. Ernst Mayr (2004) What Makes Biology Unique, page 32 and 34. And Massimo Pigliucci: "biology is an inherently historical science" (here).
  11. August Weismann (1893) wrote a book with this title (quoted by S.J. Gould (2002), page198)
  12. This threefold division is explained for example in Michael Ruse (1988) But is it Science? Chapter 8 Is There a Limit to Our Knowledge of Evolution?, pp.116-126.
  13. A course book is John Maynard Smith (1998) Evolutionary Genetics and a textbook is Daniel Hartl and Andrew Clark (1997) Principles of Population Genetics. For a historical introduction to population genetics read chapter 9 'Population Genetics' in Michael Ruse (2005) The Evolution-Creation Struggle. See further any Evolution textbook (overview).
  14. Evolution textbooks clearly explain why natural selection is not infinitely powerful. See Stearns & Hoekstra (2005), Evolution, second edition, page 44: Four factors can limit adaptation. See for an excellent discussion of constraints on adaptation par. 10.7 of Mark Ridley Evolution, third edition, pp.272-286. See also Chapter 3 'The mutational meltdown' of his Mendel's Demon (Review).
  15. "If real-world life is strongly emergent then there is truly no way to determine how genotype causes phenotype at a microscopic level." (page 86); "most real-world phenomena are completely beyond its [mathematical] grasp" (page 147) "Real world biology is too complicated" (p.295).
  16. Ludwig discusses the example of a rock: "Every microscopic irregularity must be accounted for". Although impractical to describe and although Ludwig claims it is 'a deeply philosphical question', simply applying the definition of algorithmic information content suggests a tremendous algorithmic information content. Just imagine taking a series of digital photographs of ultra-thin slices of the rock with an electron microscope. And that is just a dead rock!
  17. Mendel did not ask big questions such as 'how did flowers evolve?' but 'how is the color of a flower inherited?'. He focussed for example on the inheritance of purple and white flowers. The answers to this simple question formed the foundation for modern genetics. More about Mendel.
  18. Other critics such as physicists Fred Hoyle (posthumously published Mathematics of Evolution, review) and Lee Spetner (review) did know population genetics and tried to use it to refute neo-Darwinism. Neither was aware of Ludwig's work.
  19. Ludwig follows uncritically many ideas of Johnson and Michael Denton. Johnson himself copied a lot from Denton. Ludwig even copies examples (the lungs of birds, etc) uncritically from Denton's Evolution. A Theory in Crisis (review) while earlier in his book he pointed out that his book is not the place to evaluate the evidence for evolution.
  20. Additionally, I found the following books of Mark Allen Ludwig in Amazon: Third Paradigm: God and Government in the 21st Century (1997); Christian Revolutionary (2001); True Christian Government (2001). These books are not listed on his site. This is additional evidence that Ludwig is not only an ID advocate but also a Christian.
  21. Mathematics: Alan Turing found that it cannot be decided in a finite number of steps whether or not a computer will complete a given task in some finite number of steps (halting problem). Physics: 96% of our universe is unaccounted for. Finding the missing 96% is the single biggest challenge in physics today. Since I am not an expert in physics and mathematics, I will refrain from suggesting that the state of those sciences is abysmal!
  22. To put things into perspective, the number of neurons in the human brain is estimated to be a hundred billion.
  23. Was the 10th planet of our solar system predicted? Cosmology can predict solar eclipses with great accuracy, but it seems it can say no more than that 'there are no fundamental reasons why Pluto should not have more satellites', and indeed two additional moons around Pluto have been discovered: H. A. Weaver (2006) "Discovery of two new satellites of Pluto", Nature, 23 Feb 2006. The same holds for Uranus and probably for any planet.
  24. Weiwei Zhong and Paul W. Sternberg (2006) "Unfortunately, a genome of 20,000 genes has as many as 200 million pairwise combinations, posing a formidable challenge." SCIENCE VOL 311 10 MARCH 2006 1481.
  25. see for a short discussion of the importance of the Selfisch Gene for evolutionary biology and the relation with population genetics: Alan Grafen (2006) "The Intellectual Contribution of The Selfish Gene to Evolutionary Theory" in: Alan Grafen and Mark Ridley (2006) Richard Dawkins. How a scientist changed the way we think (2006). Please note that The Selfish Gene is not an atheïstic book. There are some criticisms of the selfish gene also. [ 9 Apr 2006 ]
  26. Robert P. Crease (2007) Six Millennia of Truth Seeking, American Scientist sept/oct 2007.
  27. Paul Davies (2007) The Goldilocks Enigma. Why is the universe just right for life? page 174 Penguin paperback. On page 175 he describes it as 'a frozen accident of history': so just as in biology, physics has its own frozen accidents! There are some more beautiful examples: Earth goes around the sun anticlockwise, but Newton cannot predict this (p.179); pencil falls in arbitrary direction (page 180).
  28. Richard Dawkins (2009) The Greatest Show on Earth p.247-248.
    The point is however, whether one is able to program a computer to simulate development. That would be a huge success. He continues: "This way of generating large and complex structures purely by the execution of local rules is deeply distinct from the blueprint way of doing things." It certainly is different from blueprints, but following local rules is exactly what computers are good at! C. elegans could be programmed on a computer because al local rules are known. Please note that Dawkins did not exclude the possibility of simulating development: "unless it it was programmed to simulate the natural biological process itself".
  29. Michael M Woolfson (2010) ON THE ORIGIN OF PLANETS By Means of Natural Simple Processes, Imperial College Press, London. (Info).
  30. Richard A. Kerr (2012) Why Is the Solar System So Bizarre? Science 1 June 2012: 1098.
  31. "The model also allowed the authors to make several predictions about cell behaviour, including that 90% of the cell's genes will be expressed in the first 2.5 hours of the approximately 9-hour cell cycle." Mark Isalan (2012) Systems biology: A cell in a computer. Nature 488, 40–41 902 August 2012)
  32. Stuart Ross Taylor (2012) Destiny or Chance Revisited: Planets and their Place in the Cosmos. The role of chance in the formation of planets.
  33. A remark in the review of Time Reborn: From the Crisis in Physics to the Future of the Universe by Lee Smolin in Nature 25 April 2013. Further: "How a system evolves is entirely encoded in the starting set of 'initial conditions' and their transformation according to the laws of physics. Evolution in time is secondary, a by-product of the theory. This bothers Smolin."
  34. Alexander F. Schier (2013) Genomics: Zebrafish earns its stripes, Nature 496, 443–444 (25 April 2013)
  35. Stability of the Solar System (wikipedia) accessed: 20 Jan 2014
  36. Although our own solar system has appeared to be stable during the few thousand years in which people have been observing the heavens (the blink of an eye on a cosmic time scale), what will happen to it in the long run remains an unsolved problem. – Three hundred years after Newton, chaos theory emerged. The real significance of chaos theory is that it gives us a very different view of the world, in which randomness and unpredictability are much more prevalent than was formerly realized. After the emergence of chaos theory, people no longer expect that scientific theories will always be able to predict the long-term behavior of complex systems with a high degree of accuracy. Paul Trow (2004) Chaos and the Solar System
  37. Later I learned that the core-accretion theory accounts for every major feature of our Solar System:
    • why all the planets orbit the Sun in the same direction
    • why their orbits are almost perfectly circular and lie in or near the plane of the star's equator
    • why the four inner planets (Mercury, Venus, Earth and Mars) are comparatively small, dense bodies made mostly of rock and iron
    • why the four outer planets (Jupiter, Saturn, Uranus and Neptune) are enormous, gaseous globes made mostly of hydrogen and helium
    However, later it was found that planetary systems, it seemed, could take any shape that did not violate the laws of physics! From: Ann Finkbeiner (2014) Astronomy: Planets in chaos, Nature, 3 July 2014. See also note 39.
  38. Philip Ball (2014) Chaos-theory pioneer nabs Abel Prize, Nature News.
  39. But progress has been made: "The fact that Mars has only 10 percent of the mass of the Earth has been a long-standing puzzle for solar system theorists. ... Here, we have a solution that arises directly from the planet formation process itself. ... this is the first model to reproduce the structure of the solar system – Earth and Venus, a small Mars, a low-mass asteroid belt, two gas giants [Jupiter, Saturn], two ice giants (Uranus and Neptune), and a pristine Kuiper Belt." Why Earth Is So Much Bigger Than Mars, Science Daily, October 27, 2015. It is not clear how many facts are explained and what is not yet explained, and what are the probabilities and the level of detail? What about Mercury?
  40. This 'three-body problem' involves the interactions of the Sun, Moon and Earth, and is much harder than predicting one planet's motion around the Sun. [10 Dec 2015]
  41. Kleomenis Tsiganis (2015) Planetary science: How the Solar System didn't form. Nature 10 Dec 2015.
    • Standard planet-formation models have been unable to reconstruct the distributions of the Solar System's small, rocky planets and asteroids in the same simulation. A new analysis suggests that it cannot be done. [10 Dec 2015]
  42. Astronomers have spotted two more moons orbiting Jupiter. The discoveries bring the moon count around the giant planet to 69. ... they formed in the distant realms of the Solar System and were later captured by the planet's gravity (Nature, Jun 8, 2017). Have these moons been predicted by theoretical models or is this unpredictable?
  43. Davide Castelvecchi (2017) Mysteries of turbulence unravelled, Nature 22 August 2017.
  44. Jupiter has 10 more moons we didn't know about – and they're weird. Nasture 19 Jul 2018. The planet now has 79 known moons, including a tiny oddball on a collision course with its neighbours.
  45. NASA fact sheet about Uranus
  46. Surprise! Pluto May Have Possessed a Subsurface Ocean at Birth, Scientific American, 23 June 2020
  47. Venus May Have Supported Life Billions of Years Ago, Space, September 23, 2019. The claim is based on new data and computer simulations. Is it true? What kind of life could have lived on Venus?
  48. Stuart Kauffman (2019) writes about ergodic systems in A World Beyond Physics.
  49. James Poskett (2022) Horizons. A global history of Science, Chapter 3 Newton's slaves (p.67/124) 8 June 2022

       Further Reading  

  • emailReply by the author Mark Ludwig to Korthof's review of his book, posted 10 June 2006.
  • Mark Ludwig about himself and his work (the page is difficult to find in Google). Contains summary of his Computer Viruses, Artificial Life and Evolution. Recommended if you want to get a quick idea of the author's goals in his own words.
  • Mark Ludwig's index page. The reviewed book can be downloaded as a self-extracting password protected file. Unfortunately, the pdf of the book (cvale.pdf) does not allow a full-text search (Adobe reader 7.0). Warning: the book is delivered together with the source codes of the viruses discussed in the book. This is not announced on Ludwig's site! Norton Antivirus detected the following 10 viruses in the zipped file cvaledsk.zip: Small.Compagnion.101 (2x), Trojan Horse, Trivial.42.A, Hacktool, TPE.RV.1600 (2x), Infector.1312, HD Trojan (p1), AEP.626. The zip file is not automatically unzipped, but Norton reads it in zipped state. Best delete the zip file. Study the contents on a standalone PC.
  • Recommended for everyone, but especially for physicists are the following books by Ernst Mayr: This is Biology; What Evolution is; What Makes Biology Unique. If Ludwig had studied those books, he would not have made the following blunder: "I am a physical scientist who is used to seeing equations that make predictions, and experiments that can test the validity of those equations. From this vantage point, evolutionary biology today appears to be unusually vacuous." (page 136). It does not harm being a physicist with mathematical training, but it certainly does not harm adapting ones approach to the subject under study.
  • Review of Tibor Gánti's Principles of Life. A groundbreaking but much ignored book.
  • Andrew G. Clark (2000) 'Limits to Prediction of Phenotypes from Knowledge of Genotypes' in: Michael T. Clegg, et al (2000) Limits to Knowledge in Evolutionary Genetics (Evolutionary Biology Vol 32).
  • A paperback (now 20 years old, but still recommended for its clarity) by an expert: John Maynard Smith (1986) The Problems of Biology chapter 5 Problems of evolutionary biology in which he discusses the 'big questions' of evolutionary biology: 1) has there been time? 2) Is all change adaptive? 3) Does evolution always proceed uphill? 4) Are there 'group' adaptations?
  • Similarities and Dissimilarities of Computer Viruses and Biological Viruses (Gert Korthof)
  • Mark Perakh Sewell's Thermodynamic Failure January 2, 2006. Please note the following remark: "(A general remark: evolution theory cannot be proven or rejected by applying any mathematical equations or laws of physics. ET is an empirical science based on immense experimental and observational material. The fact of evolution has been established beyond a reasonable doubt, although mechanisms of evolution continue to be discussed by evolutionary biologists. If certain mathematical equations or laws of physics seem to contradict ET, the reasonable explanation is that the equations or laws in question have been misapplied or misinterpreted.)"
  • To catch a glimpse of the proces of the origin of new species I recommend Menno Schilthuizen (2001) Frogs, flies & dandelions. After reading this book it will be clear that the origin of species is not a matter of computation, but observation, data collection, analysis and experiment.
  • David Sloan Wilson (2005) Essay Evolution for Everyone: How to Increase Acceptance of, Interest in, and Knowledge about Evolution. A success story about teaching evolution: when presented as unthreatening, explanatory, and useful, evolution can be easily appreciated by most people, regardless of their religious and political beliefs or prior knowledge of evolution.
  • See also section Artificial Life & Evolution of the Introduction page.
  • Another reason why evolution seems to lack creative power is that evolutionists ignored the creative power of evolution or did not know how to study it. See review of The Origin of Animal Body Plans. A study in Evolutionary Developmental Biology.
  • Richard A. Watson (2006) Compositional Evolution : The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (Vienna Series in Theoretical Biology) (Hardcover) The MIT Press (February 17, 2006). From Amazon: "In Compositional Evolution, Richard Watson uses the tools of computer science and computational biology to show that certain mechanisms of genetic variation (such as sex, gene transfer, and symbiosis) allowing the combination of preadapted genetic material enable an evolutionary process, compositional evolution, that is algorithmically distinct from the Darwinian gradualist framework."
  • Walter M. Elsasser (1998) Reflections on a Theory of Organisms, The Johns Hopkins University Press. "Elsasser argues instead that the structural complexity of even a single living cell is "transcomputational"--that is, beyond the power of any imaginable system to compute". (publisher info).
  • Claus O. Wilke and Christoph Adami (2002) The biology of digital organisms, Trends in Ecology and Evolution, 17, 11, 1 Nov 2002, 528-532.
  • Roger Brent and Jehoshua Bruck (2006) "Can computers help to explain biology?" NATURE|Vol 440|23 March 2006 p.416-417. The road leading from computer formalisms to explaining biological function will be difficult, but Roger Brent and Jehoshua Bruck suggest three hopeful paths that could take us closer to this goal.
  • Mikhail Burtsev & Peter Turchin (2006) "Evolution of cooperative strategies from first principles", Nature Vol 440|20 April 2006 p1041-1044. Is a nice example of an evolutionary artificial life model with no predetermined behaviors; the process of evolution constructs them from elementary actions.
  • The Digital Life Laboratory: "We have developed a computational system, the Avida software, which can be used to study certain basic properties of simple living systems, namely those that do not depend on the particular embodiment of information storage and machinery. Avida creates an environment within any standard computer in which populations of computer programs can live, evolve, and adapt. These programs can be thought of as a form of domesticated computer viruses..."! [ 30 Jun 06 ]
  • Greg Miller (2006) "A Scientist's Nightmare: Software Problem Leads to Five Retractions", 22 December 2006 VOL 314 SCIENCE 1856-1857 www.sciencemag.org. This nightmare is also a risk for a field of research which relies 100% on software.
  • Bertram G. Murray Jr. (2001) Are ecological and evolutionary theories scientific? Biological Reviews (2001), 76: 255-289. "I argue that theoretical biology (concerned with unobservables, such as fitness and natural selection) is not scientific because it lacks universal laws and predictive theory."
  • Computational Biology: Open Access online journal. Frequently articles about computational evolution.
  • An Introduction to Chaos: Newton tried to solve the problem of two "Suns" and a single "Earth." (The Three Body Gravitational Problem). To his surprise, he failed to find a solution to this second-simplest gravitational system. Through the years, others tried without success to solve this "three body problem."
  • About prediction in evolution see: Adam S. Wilkins (2007) 'Between "design" and "bricolage": Genetic networks, levels of selection, and adaptive evolution', PNAS, Published online before print May 9, 2007. Free access. "In effect, a network perspective may help transform evolutionary biology into a scientific enterprise with greater predictive capability than it has hitherto possessed."
  • Lucas Laursen (2009) 'Computational biology: Biological logic', Nature, Newsfeature, 462, 408-410 (2009): "Executable biology's real pay-off is that it can help biologists to understand the complexity of living things, whether at the level of groups of molecules, such as Kappa describes, or at that of signals sent between cells, as in the nematodes Fisher herself studies."
  • Giuseppe Longo, Maël Montévil, Stuart Kauffman (2012) No entailing laws, but enablement in the evolution of the biosphere "We wish to argue that the evolution of life marks the end of a physics world view of law entailed dynamics".

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Copyright ©G. Korthof 2006 First published 2 Feb 2006 Updated: 9 March 2024 15:53 F.R./Notes 8 Jun 2022