Science and Design

William A. Dembski

When the physics of Galileo and Newton displaced the physics of Aristotle, scientists

tried to explain the world by discovering its deterministic natural laws. When the quantum

physics of Bohr and Heisenberg in turn displaced the physics of Galileo and Newton,

scientists realized they needed to supplement their deterministic natural laws by taking into

account chance processes in their explanations of our universe. Chance and necessity, to

use a phrase made famous by Jacques Monod, thus set the boundaries of scientific

explanation.

Today, however, chance and necessity have proven insufficient to account for all scientific

phenomena. Without invoking the rightly discarded teleologies, entelechies, and vitalisms

of the past, one can still see that a third mode of explanation is required, namely, intelligent

design. Chance, necessity, and design—these three modes of explanation—are needed to

explain the full range of scientific phenomena.

Not all scientists see that excluding intelligent design artificially restricts science, however.

Richard Dawkins, an arch-Darwinist, begins his book The Blind Watchmaker by stating,

"Biology is the study of complicated things that give the appearance of having been

designed for a purpose." Statements like this echo throughout the biological literature. In

What Mad Pursuit, Francis Crick, Nobel laureate and codiscoverer of the structure of

DNA, writes, "Biologists must constantly keep in mind that what they see was not

designed, but rather evolved."

The biological community thinks it has accounted for the apparent design in nature through

the Darwinian mechanism of random mutation and natural selection. The point to

appreciate, however, is that in accounting for the apparent design in nature, biologists

regard themselves as having made a successful scientific argument against actual design.

This is important, because for a claim to be scientifically falsifiable, it must have the

possibility of being true. Scientific refutation is a double-edged sword. Claims that are

refuted scientifically may be wrong, but they are not necessarily wrong—they cannot

simply be dismissed out of hand.

To see this, consider what would happen if microscopic examination revealed that every

cell was inscribed with the phrase "Made by Yahweh." Of course cells don’t have "Made

by Yahweh" inscribed on them, but that’s not the point. The point is that we wouldn’t

know this unless we actually looked at cells under the microscope. And if they were so

inscribed, one would have to entertain the thought, as a scientist, that they actually were

made by Yaweh. So even those who do not believe in it tacitly admit that design always

remains a live option in biology. A priori prohibitions against design are philosophically

unsophisticated and easily countered. Nonetheless, once we admit that design cannot be

excluded from science without argument, a weightier question remains: Why should we

want to admit design into science?

To answer this question, let us turn it around and ask instead, Why shouldn’t we want to

admit design into science? What’s wrong with explaining something as designed by an

intelligent agent? Certainly there are many everyday occurrences that we explain by

appealing to design. Moreover, in our workaday lives it is absolutely crucial to distinguish

accident from design. We demand answers to such questions as, Did she fall or was she

pushed? Did someone die accidentally or commit suicide? Was this song conceived

independently or was it plagiarized? Did someone just get lucky on the stock market or

was there insider trading?

Not only do we demand answers to such questions, but entire industries are devoted to

drawing the distinction between accident and design. Here we can include forensic

science, intellectual property law, insurance claims investigation, cryptography, and

random number generation—to name but a few. Science itself needs to draw this

distinction to keep itself honest. Just last January there was a report in Science that a

Medline websearch uncovered a "paper published in Zentralblatt für Gynäkologie in

1991 [containing] text that is almost identical to text from a paper published in 1979 in the

Journal of Maxillofacial Surgery." Plagiarism and data falsification are far more

common in science than we would like to admit. What keeps these abuses in check is our

ability to detect them.

If design is so readily detectable outside science, and if its detectability is one of the key

factors keeping scientists honest, why should design be barred from the content of

science? Why do Dawkins and Crick feel compelled to constantly remind us that biology

studies things that only appear to be designed, but that in fact are not designed? Why

couldn’t biology study things that are designed?

The biological community’s response to these questions has been to resist design

absolutely. The worry is that for natural objects (unlike human artifacts) the distinction

between design and non-design cannot be reliably drawn. Consider, for instance, the

following remark by Darwin in the concluding chapter of his Origin of Species: "Several

eminent naturalists have of late published their belief that a multitude of reputed species in

each genus are not real species; but that other species are real, that is, have been

independently created. . . . Nevertheless they do not pretend that they can define, or even

conjecture, which are the created forms of life, and which are those produced by

secondary laws. They admit variation as a vera causa in one case, they arbitrarily reject it

in another, without assigning any distinction in the two cases." Biologists worry about

attributing something to design (here identified with creation) only to have it overturned

later; this widespread and legitimate concern has prevented them from using intelligent

design as a valid scientific explanation.

Though perhaps justified in the past, this worry is no longer tenable. There now exists a

rigorous criterion—complexity-specification—for distinguishing intelligently caused

objects from unintelligently caused ones. Many special sciences already use this criterion,

though in a pre-theoretic form (e.g., forensic science, artificial intelligence, cryptography,

archeology, and the Search for Extra-Terrestrial Intelligence). The great breakthrough in

philosophy of science and probability theory of recent years has been to isolate and make

precise this criterion. Michael Behe’s criterion of irreducible complexity for establishing

the design of biochemical systems is a special case of the complexity-specification

criterion for detecting design (cf. Behe’s book Darwin’s Black Box).

What does this criterion look like? Although a detailed explanation and justification is

fairly technical (for a full account see my book The Design Inference, published by

Cambridge University Press), the basic idea is straightforward and easily illustrated.

Consider how the radio astronomers in the movie Contact detected an extraterrestrial

intelligence. This movie, which came out last year and was based on a novel by Carl

Sagan, was an enjoyable piece of propaganda for the SETI research program—the

Search for Extra-Terrestrial Intelligence. In the movie, the SETI researchers found

extraterrestrial intelligence. (The nonfictional researchers have not been so successful.)

How, then, did the SETI researchers in Contact find an extraterrestrial intelligence? SETI

researchers monitor millions of radio signals from outer space. Many natural objects in

space (e.g., pulsars) produce radio waves. Looking for signs of design among all these

naturally produced radio signals is like looking for a needle in a haystack. To sift through

the haystack, SETI researchers run the signals they monitor through computers

programmed with pattern-matchers. As long as a signal doesn’t match one of the pre-set

patterns, it will pass through the pattern-matching sieve (even if it has an intelligent

source). If, on the other hand, it does match one of these patterns, then, depending on the

pattern matched, the SETI researchers may have cause for celebration.

The SETI researchers in Contact found the following signal:

110111011111011111110111111111110111111111111101111111111111111101111111

111111111111011111111111111111111111011111111111111111111111111111011111

111111111111111111111111110111111111111111111111111111111111111101111111

111111111111111111111111111111111101111111111111111111111111111111111111

111111011111111111111111111111111111111111111111111111011111111111111111

111111111111111111111111111111111111011111111111111111111111111111111111

111111111111111111111111110111111111111111111111111111111111111111111111

111111111111111111111101111111111111111111111111111111111111111111111111

111111111111111111111111011111111111111111111111111111111111111111111111

111111111111111111111111111111110111111111111111111111111111111111111111

111111111111111111111111111111111111111111110111111111111111111111111111

111111111111111111111111111111111111111111111111111111111111110111111111

111111111111111111111111111111111111111111111111111111111111111111111111

111111111111111101111111111111111111111111111111111111111111111111111111

1111111111111111111111111111111111111111111111

In this sequence of 1126 bits, 1’s correspond to beats and 0’s to pauses. This sequence

represents the prime numbers from 2 to 101, where a given prime number is represented

by the corresponding number of beats (i.e., 1’s), and the individual prime numbers are

separated by pauses (i.e., 0’s).

The SETI researchers in Contact took this signal as decisive confirmation of an

extraterrestrial intelligence. What is it about this signal that decisively indicates design?

Whenever we infer design, we must establish two things—complexity and specification.

Complexity ensures that the object in question is not so simple that it can readily be

explained by chance. Specification ensures that this object exhibits the type of pattern that

is the trademark of intelligence.

To see why complexity is crucial for inferring design, consider the following sequence of

bits:

110111011111

These are the first twelve bits in the previous sequence representing the prime numbers 2,

3, and 5 respectively. Now it is a sure bet that no SETI researcher, if confronted with this

twelve-bit sequence, is going to contact the science editor at the New York Times, hold a

press conference, and announce that an extraterrestrial intelligence has been discovered.

No headline is going to read, "Aliens Master First Three Prime Numbers!"

The problem is that this sequence is much too short (i.e., has too little complexity) to

establish that an extraterrestrial intelligence with knowledge of prime numbers produced it.

A randomly beating radio source might by chance just happen to put out the sequence

"110111011111." A sequence of 1126 bits representing the prime numbers from 2 to

101, however, is a different story. Here the sequence is sufficiently long (i.e., has enough

complexity) to confirm that an extraterrestrial intelligence could have produced it.

Even so, complexity by itself isn’t enough to eliminate chance and indicate design. If I flip

a coin 1,000 times, I’ll participate in a highly complex (or what amounts to the same thing,

highly improbable) event. Indeed, the sequence I end up flipping will be one in a trillion

trillion trillion . . . , where the ellipsis needs twenty-two more "trillions." This sequence of

coin tosses won’t, however, trigger a design inference. Though complex, this sequence

won’t exhibit a suitable pattern. Contrast this with the sequence representing the prime

numbers from 2 to 101. Not only is this sequence complex, it also embodies a suitable

pattern. The SETI researcher who in the movie Contact discovered this sequence put it

this way: "This isn’t noise, this has structure."

What is a suitable pattern for inferring design? Not just any pattern will do. Some

patterns can legitimately be employed to infer design whereas others cannot. It is easy to

see the basic intuition here. Suppose an archer stands fifty meters from a large wall with

bow and arrow in hand. The wall, let’s say, is sufficiently large that the archer can’t help

but hit it. Now suppose each time the archer shoots an arrow at the wall, the archer paints

a target around the arrow so that the arrow sits squarely in the bull’s-eye. What can be

concluded from this scenario? Absolutely nothing about the archer’s ability as an archer.

Yes, a pattern is being matched; but it is a pattern fixed only after the arrow has been

shot. The pattern is thus purely ad hoc.

But suppose instead the archer paints a fixed target on the wall and then shoots at it.

Suppose the archer shoots a hundred arrows, and each time hits a perfect bull’s-eye.

What can be concluded from this second scenario? Confronted with this second scenario

we are obligated to infer that here is a world-class archer, one whose shots cannot

legitimately be explained by luck, but rather must be explained by the archer’s skill and

mastery. Skill and mastery are of course instances of design.

Like the archer who fixes the target first and then shoots at it, statisticians set what is

known as a rejection region prior to an experiment. If the outcome of an experiment falls

within a rejection region, the statistician rejects the hypothesis that the outcome is due to

chance. The pattern doesn’t need to be given prior to an event to imply design. Consider

the following cipher text:

nfuijolt ju jt mjlf b xfbtfm

Initially this looks like a random sequence of letters and spaces—initially you lack any

pattern for rejecting chance and inferring design.

But suppose next that someone comes along and tells you to treat this sequence as a

Caesar cipher, moving each letter one notch down the alphabet. Behold, the sequence

now reads,

methinks it is like a weasel

Even though the pattern is now given after the fact, it still is the right sort of pattern for

eliminating chance and inferring design. In contrast to statistics, which always tries to

identify its patterns before an experiment is performed, cryptanalysis must discover its

patterns after the fact. In both instances, however, the patterns are suitable for inferring

design.

Patterns divide into two types, those that in the presence of complexity warrant a design

inference and those that despite the presence of complexity do not warrant a design

inference. The first type of pattern is called a specification, the second a fabrication.

Specifications are the non-ad hoc patterns that can legitimately be used to eliminate

chance and warrant a design inference. In contrast, fabrications are the ad hoc patterns

that cannot legitimately be used to warrant a design inference. This distinction between

specifications and fabrications can be made with full statistical rigor (cf. The Design

Inference).

Why does the complexity-specification criterion reliably detect design? To answer this,

we need to understand what it is about intelligent agents that makes them detectable in the

first place. The principal characteristic of intelligent agency is choice. Whenever an

intelligent agent acts, it chooses from a range of competing possibilities.

This is true not just of humans and extraterrestrial intelligences, but of animals as well. A

rat navigating a maze must choose whether to go right or left at various points in the maze.

When SETI researchers attempt to discover intelligence in the radio transmissions they are

monitoring, they assume an extraterrestrial intelligence could have chosen to transmit any

number of possible patterns, and then attempt to match the transmissions they observe

with the patterns they seek. Whenever a human being utters meaningful speech, he

chooses from a range of utterable sound-combinations. Intelligent agency always entails

discrimination—choosing certain things, ruling out others.

Given this characterization of intelligent agency, how do we recognize that an intelligent

agent has made a choice? A bottle of ink spills accidentally onto a sheet of paper;

someone takes a fountain pen and writes a message on a sheet of paper. In both instances

ink is applied to paper. In both instances one among an almost infinite set of possibilities is

realized. In both instances one contingency is actualized and others are ruled out. Yet in

one instance we ascribe agency, in the other chance.

What is the relevant difference? Not only do we need to observe that a contingency was

actualized, but we ourselves need also to be able to specify that contingency. The

contingency must conform to an independently given pattern, and we must be able

independently to formulate that pattern. A random ink blot is unspecifiable; a message

written with ink on paper is specifiable. Wittgenstein in Culture and Value made the same

point: "We tend to take the speech of a Chinese for inarticulate gurgling. Someone who

understands Chinese will recognize language in what he hears."

In hearing a Chinese utterance, someone who understands Chinese not only recognizes

that one from a range of all possible utterances was actualized, but he is also able to

identify the utterance as coherent Chinese speech. Contrast this with someone who does

not understand Chinese. He will also recognize that one from a range of possible

utterances was actualized, but this time, because he lacks the ability to understand

Chinese, he is unable to tell whether the utterance was coherent speech.

To someone who does not understand Chinese, the utterance will appear gibberish.

Gibberish—the utterance of nonsense syllables uninterpretable within any natural

language—always actualizes one utterance from the range of possible utterances.

Nevertheless, gibberish, by corresponding to nothing we can understand in any language,

also cannot be specified. As a result, gibberish is never taken for intelligent

communication, but always for what Wittgenstein calls "inarticulate gurgling."

Experimental psychologists who study animal learning and behavior employ a similar

method. To learn a task an animal must acquire the ability to actualize behaviors suitable

for the task as well as the ability to rule out behaviors unsuitable for the task. Moreover,

for a psychologist to recognize that an animal has learned a task, it is necessary not only to

observe the animal making the appropriate discrimination, but also to specify this

discrimination.

Thus to recognize whether a rat has successfully learned how to traverse a maze, a

psychologist must first specify which sequence of right and left turns conducts the rat out

of the maze. No doubt, a rat randomly wandering a maze also discriminates a sequence of

right and left turns. But by randomly wandering the maze, the rat gives no indication that it

can discriminate the appropriate sequence of right and left turns for exiting the maze.

Consequently, the psychologist studying the rat will have no reason to think the rat has

learned how to traverse the maze. Only if the rat executes the sequence of right and left

turns specified by the psychologist will the psychologist recognize that the rat has learned

how to traverse the maze.

Note that complexity is implicit here as well. To see this, consider again a rat traversing a

maze, but now take a very simple maze in which two right turns conduct the rat out of the

maze. How will a psychologist studying the rat determine whether it has learned to exit the

maze? Just putting the rat in the maze will not be enough. Because the maze is so simple,

the rat could by chance just happen to take two right turns, and thereby exit the maze.

The psychologist will therefore be uncertain whether the rat actually learned to exit this

maze, or whether the rat just got lucky.

But contrast this now with a complicated maze in which a rat must take just the right

sequence of left and right turns to exit the maze. Suppose the rat must take one hundred

appropriate right and left turns, and that any mistake will prevent the rat from exiting the

maze. A psychologist who sees the rat take no erroneous turns and in short order exit the

maze will be convinced that the rat has indeed learned how to exit the maze, and that this

was not dumb luck.

This general scheme for recognizing intelligent agency is but a thinly disguised form of the

complexity-specification criterion. In general, to recognize intelligent agency we must

observe a choice among competing possibilities, note which possibilities were not chosen,

and then be able to specify the possibility that was chosen. What’s more, the competing

possibilities that were ruled out must be live possibilities, and sufficiently numerous (hence

complex) so that specifying the possibility that was chosen cannot be attributed to chance.

All the elements in this general scheme for recognizing intelligent agency (i.e., choosing,

ruling out, and specifying) find their counterpart in the complexity-specification criterion. It

follows that this criterion formalizes what we have been doing right along when we

recognize intelligent agency. The complexity-specification criterion pinpoints what we

need to be looking for when we detect design.

Perhaps the most compelling evidence for design in biology comes from biochemistry. In a

recent issue of Cell (February 8, 1998), Bruce Alberts, president of the National

Academy of Sciences, remarked, "The entire cell can be viewed as a factory that contains

an elaborate network of interlocking assembly lines, each of which is composed of large

protein machines. . . . Why do we call the large protein assemblies that underlie cell

function machines? Precisely because, like the machines invented by humans to deal

efficiently with the macroscopic world, these protein assemblies contain highly

coordinated moving parts."

Even so, Alberts sides with the majority of biologists in regarding the cell’s marvelous

complexity as only apparently designed. The Lehigh University biochemist Michael Behe

disagrees. In Darwin’s Black Box (1996), Behe presents a powerful argument for actual

design in the cell. Central to his argument is his notion of irreducible complexity. A

system is irreducibly complex if it consists of several interrelated parts so that removing

even one part completely destroys the system’s function. As an example of irreducible

complexity Behe offers the standard mousetrap. A mousetrap consists of a platform, a

hammer, a spring, a catch, and a holding bar. Remove any one of these five components,

and it is impossible to construct a functional mousetrap.

Irreducible complexity needs to be contrasted with cumulative complexity. A system is

cumulatively complex if the components of the system can be arranged sequentially so that

the successive removal of components never leads to the complete loss of function. An

example of a cumulatively complex system is a city. It is possible successively to remove

people and services from a city until one is down to a tiny village—all without losing the

sense of community, the city’s "function."

From this characterization of cumulative complexity, it is clear that the Darwinian

mechanism of natural selection and random mutation can readily account for cumulative

complexity. Darwin’s account of how organisms gradually become more complex as

favorable adaptations accumulate is the flip side of the city in our example from which

people and services are removed. In both cases, the simpler and more complex versions

both work, only less or more effectively.

But can the Darwinian mechanism account for irreducible complexity? Certainly, if

selection acts with reference to a goal, it can produce irreducible complexity. Take Behe’s

mousetrap. Given the goal of constructing a mousetrap, one can specify a goal-directed

selection process that in turn selects a platform, a hammer, a spring, a catch, and a holding

bar, and at the end puts all these components together to form a functional mousetrap.

Given a pre-specified goal, selection has no difficulty producing irreducibly complex

systems.

But the selection operating in biology is Darwinian natural selection. And by definition this

form of selection operates without goals, has neither plan nor purpose, and is wholly

undirected. The great appeal of Darwin’s selection mechanism was, after all, that it would

eliminate teleology from biology. Yet by making selection an undirected process, Darwin

drastically reduced the type of complexity biological systems could manifest. Henceforth

biological systems could manifest only cumulative complexity, not irreducible complexity.

As Behe explains in Darwin’s Black Box, "An irreducibly complex system cannot be

produced . . . by slight, successive modifications of a precursor system, because any

precursor to an irreducibly complex system that is missing a part is by definition

nonfunctional. . . . Since natural selection can only choose systems that are already

working, then if a bio logical system cannot be produced gradually it would have to arise

as an integrated unit, in one fell swoop, for natural selection to have anything to act on."

For an irreducibly complex system, function is attained only when all components of the

system are in place simultaneously. It follows that natural selection, if it is going to produce

an irreducibly complex system, has to produce it all at once or not at all. This would not

be a problem if the systems in question were simple. But they’re not. The irreducibly

complex biochemical systems Behe considers are protein machines consisting of

numerous distinct proteins, each indispensable for function; together they are beyond what

natural selection can muster in a single generation.

One such irreducibly complex biochemical system that Behe considers is the bacterial

flagellum. The flagellum is a whip-like rotary motor that enables a bacterium to navigate

through its environment. The flagellum includes an acid-powered rotary engine, a stator,

O-rings, bushings, and a drive shaft. The intricate machinery of this molecular motor

requires approximately fifty proteins. Yet the absence of any one of these proteins results

in the complete loss of motor function.

The irreducible complexity of such biochemical systems cannot be explained by the

Darwinian mechanism, nor indeed by any naturalistic evolutionary mechanism proposed to

date. Moreover, because irreducible complexity occurs at the biochemical level, there is

no more fundamental level of biological analysis to which the irreducible complexity of

biochemical systems can be referred, and at which a Darwinian analysis in terms of

selection and mutation can still hope for success. Undergirding biochemistry is ordinary

chemistry and physics, neither of which can account for biological information. Also,

whether a biochemical system is irreducibly complex is a fully empirical question:

Individually knock out each protein constituting a biochemical system to determine

whether function is lost. If so, we are dealing with an irreducibly complex system.

Experiments of this sort are routine in biology.

The connection between Behe’s notion of irreducible complexity and my

complexity-specification criterion is now straightforward. The irreducibly complex systems

Behe considers require numerous components specifically adapted to each other and each

necessary for function. That means they are complex in the sense required by the

complexity-specification criterion.

Specification in biology always makes reference in some way to an organism’s function.

An organism is a functional system comprising many functional subsystems. The

functionality of organisms can be specified in any number of ways. Arno Wouters does so

in terms of the viability of whole organisms, Michael Behe in terms of the minimal

function of biochemical systems. Even Richard Dawkins will admit that life is specified

functionally, for him in terms of the reproduction of genes. Thus in The Blind

Watchmaker Dawkins writes, "Complicated things have some quality, specifiable in

advance, that is highly unlikely to have been acquired by random chance alone. In the

case of living things, the quality that is specified in advance is . . . the ability to propagate

genes in reproduction."

So there exists a reliable criterion for detecting design strictly from observational features

of the world. This criterion belongs to probability and complexity theory, not to

metaphysics and theology. And although it cannot achieve logical demonstration, it does

achieve a statistical justification so compelling as to demand assent. This criterion is

relevant to biology. When applied to the complex, information-rich structures of biology, it

detects design. In particular, we can say with the weight of science behind us that the

complexity-specification criterion shows Michael Behe’s irreducibly complexbiochemical

systems to be designed.

What are we to make of these developments? Many scientists remain unconvinced. Even

if we have a reliable criterion for detecting design, and even if that criterion tells us that

biological systems are designed, it seems that determining a biological system to be

designed is akin to shrugging our shoulders and saying God did it. The fear is that

admitting design as an explanation will stifle scientific inquiry, that scientists will stop

investigating difficult problems because they have a sufficient explanation already.

But design is not a science stopper. Indeed, design can foster inquiry where traditional

evolutionary approaches obstruct it. Consider the term "junk DNA." Implicit in this term is

the view that because the genome of an organism has been cobbled together through a

long, undirected evolutionary process, the genome is a patchwork of which only limited

portions are essential to the organism. Thus on an evolutionary view we expect a lot of

useless DNA. If, on the other hand, organisms are designed, we expect DNA, as much as

possible, to exhibit function. And indeed, the most recent findings suggest that designating

DNA as "junk" merely cloaks our current lack of knowledge about function. For instance,

in a recent issue of the Journal of Theoretical Biology, John Bodnar describes how

"non-coding DNA in eukaryotic genomes encodes a language which programs organismal

growth and development." Design encourages scientists to look for function where

evolution discourages it.

Or consider vestigial organs that later are found to have a function after all. Evolutionary

biology texts often cite the human coccyx as a "vestigial structure" that hearkens back to

vertebrate ancestors with tails. Yet if one looks at a recent edition of Gray’s Anatomy,

one finds that the coccyx is a crucial point of contact with muscles that attach to the pelvic

floor. The phrase "vestigial structure" often merely cloaks our current lack of knowledge

about function. The human appendix, formerly thought to be vestigial, is now known to be

a functioning component of the immune system.

Admitting design into science can only enrich the scientific enterprise. All the tried and true

tools of science will remain intact. But design adds a new tool to the scientist’s

explanatory tool chest. Moreover, design raises a whole new set of research questions.

Once we know that something is designed, we will want to know how it was produced,

to what extent the design is optimal, and what is its purpose. Note that we can detect

design without knowing what something was designed for. There is a room at the

Smithsonian filled with objects that are obviously designed but whose specific purpose

anthropologists do not understand.

Design also implies constraints. An object that is designed functions within certain

constraints. Transgress those constraints and the object functions poorly or breaks.

Moreover, we can discover those constraints empirically by seeing what does and doesn’t

work. This simple insight has tremendous implications not just for science but also for

ethics. If humans are in fact designed, then we can expect psychosocial constraints to be

hardwired into us. Transgress those constraints, and we as well as our society will suffer.

There is plenty of empirical evidence to suggest that many of the attitudes and behaviors

our society promotes undermine human flourishing. Design promises to reinvigorate that

ethical stream running from Aristotle through Aquinas known as natural law.

By admitting design into science, we do much more than simply critique scientific

reductionism. Scientific reductionism holds that everything is reducible to scientific

categories. Scientific reductionism is self-refuting and easily seen to be self-refuting. The

existence of the world, the laws by which the world operates, the intelligibility of the

world, and the unreasonable effectiveness of mathematics for comprehending the world

are just a few of the questions that science raises, but that science is incapable of

answering.

Simply critiquing scientific reductionism, however, is not enough. Critiquing reductionism

does nothing to change science. And it is science that must change. By eschewing design,

science has for too long operated with an inadequate set of conceptual categories. This

has led to a constricted vision of reality, skewing how science understands not just the

world, but also human beings.

Martin Heidegger remarked in Being and Time that "a science’s level of development is

determined by the extent to which it is capable of a crisis in its basic concepts." The basic

concepts with which science has operated these last several hundred years are no longer

adequate, certainly not in an information age, certainly not in an age where design is

empirically detectable. Science faces a crisis of basic concepts. The way out of this crisis

is to expand science to include design. To admit design into science is to liberate science,

freeing it from restrictions that can no longer be justified.

William A. Dembski, a mathematician and philosopher, is a fellow of the Center for the

Renewal of Science and Culture at the Seattle-based Discovery Institute. His new book,

The Design Inference, has just been published by Cambridge University Press.