Contents
Preface
1 The Third Mode of Explanation
1.1 Necessity, Chance, and Design
1.2 Rehabilitating Design
1.3 The Complexity-Specification Criterion
1.4 Specification
1.5 Probabilistic Resources
1.6 False Negatives and False Positives
1.7 Why the Criterion Works
1.8 The Darwinian Challenge to Design
1.9 The Constraining of Contingency
1.10 The Darwinian Extrapolation
2 Another Way to Detect Design?
2.1 Fisher's Approach to Eliminating Chance
2.2 Generalizing Fisher's Approach
2.3 Case Study: Nicholas Caputo
2.4 Case Study: The Compressibility of Bit Strings
2.5 Detachability
2.6 Sweeping the Field of Chance Hypotheses
2.7 Justifying the Generalization
2.8 The Inflation of Probabilistic Resources
2.9 Design by Comparison
2.10 Design by Elimination
3 Specified Complexity as Information
3.1 Information
3.2 Syntactic, Statistical, and Algorithmic Information
3.3 Information in Context
3.4 Conceptual and Physical Information
3.5 Complex Specified Information
3.6 Semantic Information
3.7 Biological Information
3.8 The Origin of Complex Specified Information
3.9 The Law of Conservation of Information
3.10 A Fourth Law of Thermodynamics?
4 Evolutionary Algorithms
4.1 METHINKS IT IS LIKE A WEASEL
4.2 Optimization
4.3 Statement of the Problem
4.4 Choosing the Right Fitness Function
4.5 Blind Search
4.6 The No Free Lunch Theorems
4.7 The Displacement Problem
4.8 Darwinian Evolution in Nature
4.9 Following the Information Trail
4.10 Coevolving Fitness Landscapes
5 The Emergence of Irreducibly Complex Systems
5.1 The Causal Specificity Problem
5.2 The Challenge of Irreducible Complexity
5.3 Scaffolding and Roman Arches
5.4 Co-optation, Patchwork, and Bricolage
5.5 Incremental Indispensability
5.6 Reducible Complexity
5.7 Miscellaneous Objections
5.8 The Logic of Invariants
5.9 Fine-Tuning Irreducible Complexity
5.10 Doing the Calculation
6 Design as a Scientific Research Program
6.1 Outline of a Positive Research Program
6.2 The Pattern of Evolution
6.3 The Incompleteness of Natural Laws
6.4 Does Specified Complexity Have a Mechanism?
6.5 The Nature of Nature
6.6 Must All Design in Nature Be Front-Loaded?
6.7 Embodied and Unembodied Designers
6.8 Who Designed the Designer?
6.9 Testability
6.10 Magic, Mechanism, and Design
Notes
Index
Preface
How a designer gets from thought to thing is, at least in broad strokes, straightforward: (1) A designer conceives a purpose. (2) To accomplish that purpose, the designer forms a plan. (3) To execute the plan, the designer specifies building materials and assembly instructions. (4) Finally, the designer or some surrogate applies the assembly instructions to the building materials. What emerges is a designed object, and the designer is successful to the degree that the object fulfills the designer's purpose. In the case of human designers, this four-part design process is uncontroversial. Baking a cake, driving a car, embezzling funds, and building a supercomputer each presuppose it. Not only do we repeatedly engage in this four-part design process, but we have witnessed other people engage in it countless times. Given a sufficiently detailed causal history, we are able to track this process from start to finish.
But suppose a detailed causal history is lacking and we are not able to track
the design process. Suppose instead that all we have is an object, and we must
decide whether it emerged from such a design process. In that case, how do we
decide whether the object is in fact designed? If the object in question is
sufficiently like other objects that we know were designed, then there may be
no difficulty inferring design. For instance, if we find a scrap of paper with
writing on it, we infer a human author even if we know nothing about the paper's
causal history. We are all familiar with humans writing on scraps of paper,
and there is no reason to suppose that this scrap of paper requires a different
type of causal story.
Nevertheless, when it comes to living things, the biological community holds
that a very different type of causal story is required. To be sure, the biological
community admits that biological systems appear to be designed. For instance,
Richard Dawkins writes, "Biology is the study of complicated things that
give the appearance of having been designed for a purpose." Likewise, Francis
Crick writes, "Biologists must constantly keep in mind that what they see
was not designed, but rather evolved." Or consider the title of Renato
Dulbecco's biology text: The Design of Life. The term "design" is
everywhere in the biological literature. Even so, its use is carefully regulated.
According to the biological community, the appearance of design in biology is
misleading. This is not to deny that biology is filled with marvelous contrivances.
Biologists readily admit as much. Yet as far as the biological community is
concerned, living things are not the result of the four-part design process
described above.
But how does the biological community know that living things are only apparently
and not actually designed? According to Francisco Ayala, Charles Darwin provided
the answer: "The functional design of organisms and their features would
therefore seem to argue for the existence of a designer. It was Darwin's greatest
accomplishment to show that the directive organization of living beings can
be explained as the result of a natural process, natural selection, without
any need to resort to a Creator or other external agent. The origin and adaptation
of organisms in their profusion and wondrous variations were thus brought into
the realm of science." Is it really the case, however, that the directive
organization of living beings can be explained without recourse to a designer?
And would employing a designer in biological explanations necessarily take us
out of the realm of science? The purpose of this book is to answer these two
questions.
The title of this book, No Free Lunch, refers to a collection of mathematical
theorems proved in the past five years about evolutionary algorithms. The upshot
of these theorems is that evolutionary algorithms, far from being universal
problem solvers, are in fact quite limited problem solvers that depend crucially
on additional information not inherent in the algorithms before they are able
to solve any interesting problems. This additional information needs to be carefully
specified and fine-tuned, and such specification and fine-tuning is always thoroughly
teleological. Consequently, evolutionary algorithms are incapable of providing
a computational justification for the Darwinian mechanism of natural selection
and random variation as the primary creative force in biology. The subtitle,
Why Specified Complexity Cannot Be Purchased without Intelligence, refers to
that form of information, known as specified complexity or complex specified
information, that is increasingly coming to be regarded as a reliable empirical
marker of purpose, intelligence, and design.
What is specified complexity? An object, event, or structure exhibits specified
complexity if it is both complex (i.e., one of many live possibilities) and
specified (i.e., displays an independently given pattern). A long sequence of
randomly strewn Scrabble pieces is complex without being specified. A short
sequence spelling the word "the" is specified without being complex.
A sequence corresponding to a Shakespearean sonnet is both complex and specified.
In The Design Inference: Eliminating Chance through Small Probabilities, I argued
that specified complexity is a reliable empirical marker of intelligence. Nevertheless,
critics of my argument have claimed that evolutionary algorithms, and the Darwinian
mechanism in particular, can deliver specified complexity apart from intelligence.
I anticipated this criticism in The Design Inference but did not address it
there in detail. Filling in the details is the task of the present volume.
The Design Inference laid the groundwork. This book demonstrates the inadequacy
of the Darwinian mechanism to generate specified complexity. Darwinists themselves
have made possible such a refutation. By assimilating the Darwinian mechanism
to evolutionary algorithms, they have invited a mathematical assessment of the
power of the Darwinian mechanism to generate life's diversity. Such an assessment,
begun with the No Free Lunch theorems of David Wolpert and William Macready
(see section 4.6), will in this book be taken to its logical conclusion. The
conclusion is that Darwinian mechanisms of any kind, whether in nature or in
silico, are in principle incapable of generating specified complexity. Coupled
with the growing evidence in cosmology and biology that nature is chock-full
of specified complexity (cf. the fine-tuning of cosmological constants and the
irreducible complexity of biochemical systems), this conclusion implies that
naturalistic explanations are incomplete and that design constitutes a legitimate
and fundamental mode of scientific explanation.
In arguing that naturalistic explanations are incomplete or, equivalently, that
natural causes cannot account for all the features of the natural world, I am
placing natural causes in contradistinction to intelligent causes. The scientific
community has itself drawn this distinction in its use of these twin categories
of causation. Thus, in the quote earlier by Francisco Ayala, "Darwin's
greatest accomplishment [was] to show that the directive organization of living
beings can be explained as the result of a natural process, natural selection,
without any need to resort to a Creator or other external agent." Natural
causes, as the scientific community understands them, are causes that operate
according to deterministic and nondeterministic laws and that can be characterized
in terms of chance, necessity, or their combination (cf. Jacques Monod's Chance
and Necessity). To be sure, if one is more liberal about what one means by natural
causes and includes among natural causes telic processes that are not reducible
to chance and necessity (as the ancient Stoics did by endowing nature with immanent
teleology), then my claim that natural causes are incomplete dissolves. But
that is not how the scientific community by and large understands natural causes.
The distinction between natural and intelligent causes now raises an interesting
question when it comes to embodied intelligences like ourselves, who are at
once physical systems and intelligent agents: Are embodied intelligences natural
causes? Even if the actions of an embodied intelligence proceed solely by natural
causes, being determined entirely by the constitution and dynamics of the physical
system that embodies it, that does not mean the origin of that system can be
explained by reference solely to natural causes. Such systems could exhibit
derived intentionality in which the underlying source of intentionality is irreducible
to natural causes (cf. a digital computer). I will argue that intelligent agency,
even when conditioned by a physical system that embodies it, cannot be reduced
to natural causes without remainder. Moreover, I will argue that specified complexity
is precisely the remainder that remains unaccounted for. Indeed, I will argue
that the defining feature of intelligent causes is their ability to create novel
information and, in particular, specified complexity.
Design has had a turbulent intellectual history. The chief difficulty with design
to date has consisted in discovering a conceptually powerful formulation of
it that will fruitfully advance science. While I fully grant that the history
of design arguments warrants misgivings, they do not apply to the present project.
The theory of design I envision is not an atavistic return to the design arguments
of William Paley and the Bridgewater Treatises. William Paley was in no position
to formulate the conceptual framework for design that I will be developing in
this book. This new framework depends on advances in probability theory, computer
science, the concept of information, molecular biology, and the philosophy of
science-to name but a few. Within this framework design promises to become an
effective conceptual tool for investigating and understanding the world.
Increased philosophical and scientific sophistication, however, is not alone
in separating my approach to design from Paley's. Paley's approach was closely
linked to his prior religious and metaphysical commitments. Mine is not. Paley's
designer was nothing short of the triune God of Christianity, a transcendent,
personal, moral being with all the perfections commonly attributed to this God.
On the other hand, the designer that emerges from a theory of intelligent design
is an intelligence capable of originating the complexity and specificity that
we find throughout the cosmos and especially in biological systems. Persons
with theological commitments can co-opt this designer and identify this designer
with the object of their worship. But this move is strictly optional as far
as the actual science of intelligent design is concerned.
The crucial question for science is whether design helps us understand the world,
and especially the biological world, better than we do now when we systematically
eschew teleological notions from our scientific theorizing. Thus, a scientist
may view design and its appeal to a designer as simply a fruitful device for
understanding the world, not attaching any significance to questions such as
whether a theory of design is in some ultimate sense true or whether the designer
actually exists. Philosophers of science would call this a constructive empiricist
approach to design. Scientists in the business of manufacturing theoretical
entities like quarks, strings, and cold dark matter could therefore view the
designer as just one more theoretical entity to be added to the list. I follow
here Ludwig Wittgenstein, who wrote, "What a Copernicus or a Darwin really
achieved was not the discovery of a true theory but of a fertile new point of
view." If design cannot be made into a fertile new point of view that inspires
exciting new areas of scientific investigation, then it deserves to wither and
die. Yet before that happens, it deserves a fair chance to succeed.
One of my main motivations in writing this book is to free science from arbitrary
constraints that, in my view, stifle inquiry, undermine education, turn scientists
into a secular priesthood, and in the end prevent intelligent design from receiving
a fair hearing. The subtitle of Richard Dawkins's The Blind Watchmaker reads
Why the Evidence of Evolution Reveals a Universe without Design. Dawkins may
be right that design is absent from the universe. But science needs to address
not only the evidence that reveals the universe to be without design but also
the evidence that reveals the universe to be with design. Evidence is a two-edged
sword: claims capable of being refuted by evidence are also capable of being
supported by evidence. Even if design ends up being rejected as an unfruitful
explanatory tool for science, such a negative outcome for design needs to result
from the evidence for and against design being fairly considered. Darwin himself
would have agreed: "A fair result can be obtained only by fully stating
and balancing the facts and arguments on both sides of each question."
Consequently, any rejection of design must not result from imposing arbitrary
constraints on science that rule out design prior to any consideration of evidence.
Two main constraints have historically been used to keep design outside the
natural sciences: methodological naturalism and dysteleology. According to methodological
naturalism, in explaining any natural phenomenon, the natural sciences are properly
permitted to invoke only natural causes to the exclusion of intelligent causes.
On the other hand, dysteleology refers to inferior design-typically design that
is either evil or incompetent. Dysteleology rules out design from the natural
sciences on account of the inferior design that nature is said to exhibit. In
this book, I will address methodological naturalism. Methodological naturalism
is a regulative principle that purports to keep science on the straight and
narrow by limiting science to natural causes. I intend to show that it does
nothing of the sort but instead constitutes a straitjacket that actively impedes
the progress of science.
On the other hand, I will not have anything to say about dysteleology. Dysteleology
might present a problem if all design in nature were wicked or incompetent and
continually flouted our moral and aesthetic yardsticks. But that is not the
case. To be sure, there are microbes that seem designed to do a number on the
mammalian nervous system and biological structures that look cobbled together
by a long trial-and-error evolutionary process. But there are also biological
examples of nano-engineering that surpass anything human engineers have concocted
or entertain hopes of concocting. Dysteleology is primarily a theological problem.
To exclude design from biology simply because not all examples of biological
design live up to our expectations of what a designer should or should not have
done is an evasion. The problem of design in biology is real and pervasive,
and needs to be addressed head on and not sidestepped because our presuppositions
about design happen to rule out imperfect design. Nature is a mixed bag. It
is not William Paley's happy world of everything in delicate harmony and balance.
It is not the widely caricatured Darwinian world of nature red in tooth and
claw. Nature contains evil design, jerry-built design, and exquisite design.
Science needs to come to terms with design as such and not dismiss it in the
name of dysteleology.
A possible terminological confusion over the phrase "intelligent design"
needs to be cleared up. The confusion centers on what the adjective "intelligent"
is doing in the phrase "intelligent design." "Intelligent"
can mean nothing more than being the result of an intelligent agent, even one
who acts stupidly. On the other hand, it can mean that an intelligent agent
acted with consummate skill and mastery. Critics of intelligent design often
understand the "intelligent" in intelligent design in the latter sense
and thus presume that intelligent design must entail optimal design. The intelligent
design community, on the other hand, understands the "intelligent"
in intelligent design simply to refer to intelligent agency (irrespective of
skill, mastery, or cleverness) and thus separates intelligent design from optimality
of design. But why then place the adjective intelligent in front of the noun
design? Does not design already include the idea of intelligent agency, so that
juxtaposing the two becomes redundant? Redundancy is avoided because intelligent
design needs also to be distinguished from apparent design. Because design in
biology so often connotes apparent design, putting intelligent in front of design
ensures that the design we are talking about is not merely apparent but also
actual. Whether that intelligence acts cleverly or stupidly, wisely or unwisely,
optimally or suboptimally are separate questions.
Who will want to read No Free Lunch? The audience includes anyone interested
in seriously exploring the scope and validity of Darwinism as well as in learning
how the emerging theory of intelligent design promises to supersede it. Napoleon
III remarked that one never destroys a thing until one has replaced it. Similarly,
Thomas Kuhn, in the language of paradigms and paradigm shifts, claimed that
for a paradigm to shift, there has to be a new paradigm in place ready to be
shifted into. Throughout my work, I have not been content merely to critique
existing theory but have instead striven to provide a positive more-encompassing
framework within which to reconceptualize phenomena inadequately explained by
existing theory. Much of No Free Lunch will be accessible to an educated lay
audience. Many of the ideas have been presented in published articles and public
lectures. I have seen how the ideas in this book have played themselves out
under fire. The chapters are therefore tailored to questions people are actually
asking. The virtue of this book is filling in the details. And the devil is
in the details.
I have tried to keep technical discussions to a minimum. I am no fan of notation-heavy
prose and avoid it whenever possible. A book of this sort, however, poses a
peculiar challenge. Forms of thinking that turn biological complexity into a
free lunch pervade science and are deeply entrenched. It does no good therefore
to speak in generalities or point to certain obvious tensions (e.g., how can
intelligence arise out of an inherently unintelligent Darwinian process? or
how can we have any confidence in the reliability of our cognitive faculties
if we are the result of a brute natural process for which survival and reproduction
is everything and truth-seeking is incidental?). Make the book too obvious,
and no one will pay it any mind. Make it too technical, and no one will read
it. My strategy in writing this book, therefore, has been to include just enough
technical discussion so that experts can fill in the details as well as sufficient
elaboration of the technical discussion so that nonexperts feel the force of
the design inference. Whether I have been successful is for others to judge.
No Free Lunch has the following logical structure. Chapter 1 presents a nontechnical
summary of my work on inferring design and makes the connection between my previous
work and Darwinism explicit. Chapter 2 rebuts critics who argue that specified
complexity is not a well-defined concept and cannot form the basis for a compelling
design inference. In particular, I offer there a simplified account of specification.
Chapter 3 translates the design-inferential framework of chapters 1 and 2 into
a more powerful information-theoretic framework. Chapter 4 shows how this information-theoretic
approach to design withstands and then overturns the challenge of evolutionary
algorithms. In particular, I show that evolutionary algorithms cannot generate
specified complexity. Chapter 5 then shows how the theoretical apparatus developed
in the previous chapters can be applied to actual biological systems. Finally,
chapter 6 examines what intelligent design means for science.
What follows is
a chapter by chapter summary of the book.
Chapter 1: The
Third Mode of Explanation. How is design empirically detectable and
thus distinguishable from the two generally accepted modes of scientific explanation,
chance and necessity? To detect design, two features must be present: complexity
and specification. Complexity guarantees that the object in question is not
so simple that it can readily be attributed to chance. Specification guarantees
that the object exhibits the right sort of pattern associated with intelligent
causes. Specified complexity thus becomes a criterion for detecting design empirically.
Having proposed a theoretical apparatus for detecting design, I next consider
the challenge that Darwin posed to design historically and indicate why his
challenge is viewed among many scientists as counting decisively against design.
Essentially, Darwin opposed to design the joint action of chance and necessity
and therewith promised to explain the complex ordered structures in biology
that prior to him were attributed to design.
Chapter 2: Another
Way to Detect Design? Many in the scientific and philosophical community
have staked their hopes on explaining specified complexity by means of evolutionary
algorithms. Yet even without evolutionary algorithms to explain specified complexity,
few are prepared to embrace design. One approach, now increasingly championed
by the philosopher of science Elliott Sober, is to attack specified complexity
head-on and claim that it is a spurious concept, incapable of rendering design
testable in the case of natural objects, and that a precise probabilistic and
complexity-theoretic analysis of specified complexity vitiates the concept entirely.
In critiquing my approach to detecting design, Sober has tied himself to a likelihood
framework for probability that is itself highly problematic. This chapter demonstrates
that specified complexity is a well-defined concept and that it readily withstands
the criticisms raised by Sober and his colleagues.
Chapter 3: Specified Complexity as Information. Intelligent design can be formulated as a theory of information. Within such a theory, specified complexity becomes a form of information that reliably signals design. As a form of information specified complexity also becomes a proper object for scientific investigation. This chapter takes the ideas of chapters 1 and 2 and translates them into an information-theoretic framework. This reframing of intelligent design within information theory powerfully extends the design-inferential framework developed in chapter 1 and makes it possible accurately to assess the power (or lack thereof) of the Darwinian mechanism. The upshot of this chapter is a conservation law governing the origin and flow of information. From this law it follows that specified complexity is not reducible to natural causes and that the origin of specified complexity is best sought in intelligent causes. Intelligent design thereby becomes a theory for detecting and measuring information, explaining its origin, and tracing its flow.
Chapter 4: Evolutionary
Algorithms. This chapter is the climax of the book. Here I examine
evolutionary algorithms, which constitute the mathematical underpinnings of
Darwinism. I show that evolutionary algorithms are in principle incapable of
generating specified complexity. Whereas this result follows immediately from
the conservation of information law in chapter 3, this law involves a high level
of abstraction, so that simply applying the law does not make clear just how
limited evolutionary algorithms really are. In this chapter I therefore examine
the nuts and bolts of the evolutionary algorithms: phase spaces, fitness landscapes,
and optimization algorithms. An elementary combinatorial analysis shows that
evolutionary algorithms can no more generate specified complexity than can five
letters fill ten mailboxes.
Chapter 5:
The Emergence of Irreducibly Complex Systems. Specified complexity
as a reliable empirical marker of intelligence is all fine and well, but if
there are no complex specified systems in nature, what then? The previous chapters
establish that specified complexity reliably signals design, not that specified
complexity is actualized in any concrete physical system. This chapter examines
how we determine whether a physical system exhibits specified complexity. The
key to this determination, at least in biology, is Michael Behe's notion of
irreducible complexity. Irreducibly complex biological systems exhibit specified
complexity. Irreducible complexity is therefore a special case of specified
complexity. Because specified complexity is a probabilistic notion, determining
whether a physical system exhibits specified complexity requires being able
to calculate probabilities. One of the objections against intelligent design
becoming a viable scientific research program is that one cannot calculate the
probabilities needed to confirm specified complexity for actual systems in nature.
This chapter shows that even though precise calculations may not always be possible,
setting bounds for the relevant probabilities is possible, and that this is
adequate for establishing specified complexity in practice.
Chapter 6: Design
as a Scientific Research Program. Having shown that specified complexity
is a reliable empirical marker of intelligence and having overturned the main
scientific objections raised against it, I conclude this book by examining what
science will look like once design is readmitted to full scientific status.
The worry is that attributing design to natural systems will stultify science
in the sense that once a scientist concedes that some natural system is designed,
all the scientist's work is over. But this is not the case. Design raises a
host of novel and interesting research questions that it does not make sense
to ask within a strictly Darwinian or naturalistic framework. One such question
is teasing apart the effects of natural and intelligent causation. For instance,
a rusted old Cadillac is clearly designed but also shows the effects of natural
causes (i.e., weathering). Intelligent design is capable of accommodating the
legitimate insights of Darwinian theory. In particular, intelligent design admits
a place for the Darwinian mechanism of natural selection and random variation.
But as a framework for doing science, intelligent design offers additional tools
for investigating nature that render it conceptually more powerful than Darwinism.
Ideally, this book should be read from start to finish. Nevertheless, because
this is not always possible, let me offer the following suggestions for reading
the book. Chapter 1 is the most accessible chapter in the book and is prerequisite
for everything that follows. This material needs to be under the reader's belt.
Sections 1.1 to 1.7 present a nontechnical summary of my previous work on inferring
design, and readers familiar with it can skip these sections without loss. On
the other hand, sections 1.8 to 1.10 are new and make explicit the connection
between my previous work and Darwinism. Readers definitely need to read these
sections. Chapter 2 is primarily directed at critics. This is the most technical
chapter, and readers persuaded by my previous work may want to skip it on their
initial reading. Chapters 3 and 4 translate the design-theoretic framework of
chapters 1 and 2 into an information-theoretic framework. Chapter 3 presents
the general theory whereas chapter 4 looks specifically at evolutionary algorithms.
For nontechnical readers, I recommend a light perusal of chapter 3 and then
a careful examination of chapter 4. Chapter 5 brings theory in contact with
biological reality. This is where most of the current controversy lies, and
readers will not want to miss this chapter. Chapter 6, on the other hand, looks
at the broader implications of intelligent design for science and can be read
at leisure.
There are nontechnical readers who can comfortably wade past technical mathematical
discussions without being intimidated; and then there are math phobics whose
eyes glaze over and brains shut down at the sight of technical mathematical
discussions. This book can also be read with profit by math phobics. I suggest
reading sections 1.1-1.10, 5.1-5.7, 5.9, and 6.1-6.10 in order. The only thing
one needs to know about mathematics to read these sections is that powers of
ten count the number of zeroes following a one. Thus 103 is 1,000 (a thousand
has three zeroes after the initial one), 106 is 1,000,000 (a million has six
zeroes after the initial one), etc. Reading these sections will provide a good
overview of the current debate regarding intelligent design, particularly as
it relates to Michael Behe's work on irreducibly complex molecular machines.
Math phobics who then want to see why evolutionary algorithms cannot do the
design work that Darwinists regularly attribute to these algorithms can read
sections 4.1-4.2 and 4.7-4.9.
One final caution: Even though much in this book will look familiar to readers
acquainted with my previous work, this familiarity can be deceiving. I have
already noted that sections 1.1 to 1.7 present a nontechnical summary of my
work on inferring design and that readers familiar with it can skip these sections
without loss. But other sections, though apparently covering old ground, in
fact differ markedly from previous work. For instance, two of my running examples
in The Design Inference were the Caputo case (an instance of apparent ballot-line
fraud) and algorithmic information theory. The case studies in sections 2.3
and 2.4 re-examine these examples in light of criticisms brought against them.
Except for chapter 1, arguments and topics revisited are in almost every instance
reworked or beefed up.
William A. Dembski
Baylor University
Waco, Texas
Copyright 2001 William A. Dembski. All rights reserved. International
copyright secured.
File Date: 10.15.01