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How can we identify events due to intelligent causes and distinguish them from events due to undirected natural causes? If we lack a causal theory how can we determine whether an intelligent cause acted? This book presents a reliable method for detecting intelligent causes: the design inference. The design inference uncovers intelligent causes by isolating the key trademark of intelligent causes: specified events of small probability. Design inferences can be found in a range of scientific pursuits from forensic science to research into the origins of life to the search for extraterrestrial intelligence. This challenging and provocative book will be read with particular interest by philosophers of science and religion, other philosophers concerned with epistemology and logic, probability and complexity theorists, and statisticians.
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"...quite readable. Those who have no knowledge of the mathematics of probability may be put off, but in fact the level of mathematics and symbolic logic employed is not very difficult...The main arguments...are given in ordinary prose, then translated into symbols...Dembski has made a real advance in probability and information theory..." Books & Culture
"...generally careful and precise, often persuasive, and at times surprisingly philosophically sensitive." Ethics
"Dembski has produced an astonishing work. The Design InferenceR^ will no doubt become the cornerstone of the intelligent design movement. A marked and dog-eared copy of The Design InferenceR^ deserves a place on your shel not just for its clear historical significance, but also to allow yourself a place in the momentous discussion to come. Philosophia Christi
Book Description
This book presents a reliable method for detecting intelligent causes: the design inference.The design inference uncovers intelligent causes by isolating the key trademark of intelligent causes: specified events of small probability. Design inferences can be found in a range of scientific pursuits from forensic science to research into the origins of life to the search for extraterrestrial intelligence. This challenging and provocative book shows how incomplete undirected causes are for science and breathes new life into classical design arguments. It will be read with particular interest by philosophers of science and religion, other philosophers concerned with epistemology and logic, probability and complexity theorists, and statisticians.
A mathematician and philosopher, William A. Dembski is Research Professor in Philosophy at Southwestern Seminary in Ft. Worth, where he directs its Center for Cultural Engagement. He is also a senior fellow with Discovery Institute's Center for Science and Culture in Seattle. Previously he was the Carl F. H. Henry Professor of Theology and Science at The Southern Baptist Theological Seminary in Louisville, where he founded its Center for Theology and Science. Before that he was Associate Research Professor in the Conceptual Foundations of Science at Baylor University, where he headed the first intelligent design think-tank at a major research university: The Michael Polanyi Center.
Dr. Dembski has taught at Northwestern University, the University of Notre Dame, and the University of Dallas. He has done postdoctoral work in mathematics at MIT, in physics at the University of Chicago, and in computer science at Princeton University. A graduate of the University of Illinois at Chicago where he earned a B.A. in psychology, an M.S. in statistics, and a Ph.D. in philosophy, he also received a doctorate in mathematics from the University of Chicago in 1988 and a master of divinity degree from Princeton Theological Seminary in 1996. He has held National Science Foundation graduate and postdoctoral fellowships.
Dr. Dembski has published articles in mathematics, engineering, philosophy, and theology journals and is the author/editor of more than a dozen books. In The Design Inference: Eliminating Chance Through Small Probabilities (Cambridge University Press, 1998), he examines the design argument in a post-Darwinian context and analyzes the connections linking chance, probability, and intelligent causation. The sequel to The Design Inference appeared with Rowman & Littlefield in 2002 and critiques Darwinian and other naturalistic accounts of evolution. It is titled No Free Lunch: Why Specified Complexity Cannot Be Purchased without Intelligence. Dr. Dembski has edited several influential anthologies, including Uncommon Dissent: Intellectuals Who Find Darwinism Unconvincing (ISI, 2004) and Debating Design: From Darwin to DNA (Cambridge University Press, 2004, co-edited with Michael Ruse). His newest book, The End of Christianity, differs markedly from his others, attempting to understand how the Fall of humanity can be real in light of modern science.
As interest in intelligent design has grown in the wider culture, Dr. Dembski has assumed the role of public intellectual. In addition to lecturing around the world at colleges and universities, he is frequently interviewed on the radio and television. His work has been cited in numerous newspaper and magazine articles, including three front page stories in the New York Times as well as the August 15, 2005 Time magazine cover story on intelligent design. He has appeared on the BBC, NPR (Diane Rehm, etc.), PBS (Inside the Law with Jack Ford; Uncommon Knowledge with Peter Robinson), CSPAN2, CNN, Fox News, ABC Nightline, and the Daily Show with Jon Stewart.
I just finished a two-month reading group consisting of both supporters and critics of Dembski, so I finally feel competent to review this book.
While I am a naturalist and evolutionist, I greatly appreciate the writing of anybody who is intellectually honest and attempts to be rigorous: at least in this book, Dembski shows these traits with flying colors. 'The Design Inference' is Dembski's attempt to formalize valid inferences about design. That is, how can we validly infer, for any event E, that E is the product of intelligent design? Most people make such inferences all the time (how does the average person explain Stonehenge). What is the logical structure of such inferences?
Despite the math, the argument structure is actually quite simple. The way to infer that E is the product of design is to run it through what Dembski calls the 'explanatory filter.' Try to explain event E according to presently known statistical regularities (e.g., Newton's laws). If event E cannot be explained by any such statistical regularity, then it passes through the explanatory filter, and is therefore the product of design.
This argument structure is the first main weakness in Dembski's book. In employing the explanatory filter, TDI elevates an anachronistic fallacy to an imperative. Simply showing that we can't presently explain a phenomenon is not sufficient to show that it can never be explained! In the nineteenth century, the precession of Mercury in its orbit could not be explained in a well-confirmed classical worldview, but to infer design based on that would not be good science.
... The problems with this kind of reasoning are made clearer when we consider our early ancestors who made poor design arguments about weather patterns and illness that they couldn't explain based on physical principles.
The inferential strategy outlined above sounds rather simple, so where does all the notorious math come in? It comes in as Dembski attempts to quantitatively unpack just how to demonstrate that an event cannot be explained by a statistical regularity. For those who know some statistics, this is essentially a detailed account of how to rationally generate a rejection region in a probability distribution. The formalism emerges because Dembski's account is idiosyncratic, as he tries to show that you can generate a rejection region even *after* you have already observed the event. Most scientists would balk at this, as it would allow you to retroactively put a rejection region over the event, which to put it simply, is cheating (imagine drawing a bull's-eye around a randomly shot arrow and saying that you hit the bull's-eye by skill).
Dembski claims that it is perfectly appropriate to retroactively generate rejection regions if it would have been *possible* to specify the region before the event E actually occurred. For example, say you see someone shoot an arrow that hits a tree at a seemingly random location where there happens to be a worm. Later, however, you find out and that the person was actually hunting worms and was wearing infrared worm-hunting goggles. In such a case, you would rightly conclude that the worm was hit because of skill rather than blind luck. More importantly, it would have been possible to predict that the arrow would land on tree-worms even if you hadn't seen it happen.
While many people in our discussion group disagreed, I think this is a reasonable way to retroactively reject a chance-based explanation. However, I do *not* think that Dembski is simply describing the rejection of a hypothesis. Rather, he is describing the replacement of one hypothesis with a more reasonable alternative (in this example, the alternative to chance is that the person is a skilled worm-hunter). This leads to what I think is the second main weakness in *The Design Inference*: the engine driving the inference is not a positive theory of design, but simply the elimination of other theories. The problem is that this does not seem to conform to how people do (or should) perform design inferences. That is, people don't run through an explanatory filter, eliminating all possible statistical explanations of something, and then end up with 'design' as the last node in an explanatory filter (or explanatory sink, as I like to call it). Rather, people have a *positive theory* of intelligent agents (i.e., things with desires, beliefs, and certain capacities) and they apply this theory (or network of theories) to explain events in the world. Design inferences are not different in kind from explanations of physical, biological, social, or psychological phenomena. It is the development of such a theory and its predictions which should be the focus for Dembski.
A final note: to those interested in the debate about creationism and evolution, caveat emptor. This book contains very little direct discussion of that issue. Rather, it does what should have been done long ago: tries to outline the inferential strategy people should be employing in this debate.
Despite the two main problems outlined above, I still recommend this book to anyone seriously interested in how we make inferences about design, in particular those interested in the creation-evolution debate. While the book does no damage whatsoever to the evolutionist (partly because, as mentioned above, it does not directly address that debate) it at least makes for stimulating, thought-provoking reading. Most importantly, it will direct the creationists to be more rigorous in their arguments about design.
This book will surely please those looking for rational support of Christian faith, and it does have some very strong points throughout. But in the end, believers will enjoy it and non-believers will find it infuriating. Dembski's work has often unfairly been described as 'thinly disguised creationism' because of his political associations, and his associations with all that awful anti-evolution rhetoric among many of his colleagues. However, his work here stands on his own in some ways.
Dembski does come up with good criteria for detecting design in nature. It is in the final step in Dembski's reasoning, how design in nature is to be explained, that reasonable people may well strongly disagree. It is in the question of the role of naturalism in explanations that we find the most difficult sticking point, as a careful analysis of Phillip Johnson's books (such as Wedge of Truth) clearly reveals.
Dembski's work here is clever, careful, and creative. He does an admirable job of deriving reasonable criteria for detecting design in nature according to information theoretic principles. I don't consider this 'junk science' as some have claimed. In the end, of course, Dembski relates his discovery of specified complexity criteria for design with the God of the Bible, an intelligent update of Paley's design argument.
The question to me when I read this was not whether Dembski succeeded in coming up with useful design criteria. I decided that he did indeed. The question for me was whether he also made a convincing argument that Darwinian mechanisms could not have resulted in specified complexity in nature.
The technical issue seems to be this. His argument seems to me to potentially confuse different kinds of information.
... After deriving his criteria for complex specified information, Dembski tells us that blind processes cannot increase complex specified information, because neither selection nor random mutation add information.
It's a persuasive argument, similar to the argument that Roger Penrose made for consciousness being irreducible to computation. And it suffers from the same weakness, that the systems we are describing are not yet competely enough described to know for certain whether randomness and selection really provide a process that can produce complex specified information. Facing this uncertainty, fans of Darwin of course consider it entirely plausible that randomness plus selection can lead to not only complexity but complex specified information in Dembski's sense. Fans of Dembski will probably find his argument compelling that Darwinian processes can never produce true randomness. And there are a third group, following complexity theory or modern developments in genetics, who find that Darwinian processes can yield complex specified information if suitably enhanced by additional laws of nature.
So Dembski's argument for design in this book seems sound, the question is still, as it always was, whether design can be explained by true novelty arising through Darwinian processes or whether true novelty cannot arise spontaneously in nature.
Understandably, this is a powerful sticking point because it reveals very different views of the inherent structure of nature, either a pre-existing design or an emerging one. In soem ways, this brings to mind our political extremes of the conservative's structured world and the liberal's dynamic free-for-all. Dembski provides us with a useful plank for the conservative religious worldview, though it won't convince those who have assumed all along that the structure of the world is a dynamic, evolving thing. These are very different visions of how the world works.
Despite Eli Chiprout's critical review of The Design Inference, readers can be assured that Dembski stands by his calculation and is prepared to defend it. Chiprout's chief objection seems to be that Dembski's conditional independence condition founders when human agents get into the act. Chiprout may register his complaint, but we should all note that this book and the theories it puts forth have been thoroughly vetted: it was Dembski's doctoral dissertation, it went through a grueling review process with Cambridge University Press, and the author sent preprints to probably fifty or so scholars and academics for comment. No one, and I mean **NO ONE**, corrected Dembski on what Chiprout suggests is an obvious oversight. Long after the dust of criticism settles, The Design Inference will surely stand as an important and enduring advancement in our understanding of the theory of Intelligent Design.
I read this book in manuscript form, and I must say that I was blown away by the range, scope, and intellectual rigor of Dembski's work. There is a tremendous amount of important, original research in this book; so much so, in fact, that it will definitely help to define the nature of the new Intelligent Design movement for years to come. Dembski is a precise and careful thinker who also writes well. I would recommend this book highly.
A recent customer review called this book "junk science", despite its having been published by Cambridge University Press in their Cambridge Studies in Probability, Induction and Decision Theory. The reader appears to misunderstand the concept of specified complexity. The sentence "It [random] implies in fact that an inferrable order is present!" simply is wrong.
Random events occur independently of each other. This means that the result of the first toss of a fair coin has no effect on the second toss of the same coin: if the first toss is heads, the probability of the second toss still is 50% heads, 50% tails. The same is true if the first toss comes out tails: 50% heads, 50% tails for the second toss. Even if the first five tosses come out heads-heads-heads-heads-heads, the probability of the sixth toss is 50% heads, 50% tails.
Thus while the reader is absolutely correct that "we can predict that a fair coin flipped a large number of times comes up heads with a frequency of about 1/2 the time", we cannot predict the sequence of individual tosses. Even just 10 tosses has a thousand possible sequences, while 25 tosses has some 30 million possible sequences. "Specified" means that you predict the sequence of tosses beforehand. Try predicting the sequence of 25 tosses: it is most unlikely (1 in ~30 million) that the tosses will come out as you predict.
The key point is that the result of the 25 tosses (for example HTTHTHHTTT HTHTHHTHHT HTTTH) is minimally informative, no more informative than random typing on a keyboard: all one can say is that a random process is going on. A thousand tosses of the coin gives you no more information than 25 tosses: one can still only say that a random process is going on.
...
Biological molecules however are organized in a manner far from random. Each amino acid in the sequence of hundreds or thousands making up a particular protein carries some information about the shape and function of the protein. There is thus more information contained in a large protein than in a small one. One cannot pick at random from the 20 or so natural amino acids in putting together a protein and expect to get a particular shape and function. The order of the amino acids is specified for each step in the process or synthesizing a particular protein (with a few exceptions for positions distant from the center of activity of the protein).
Thus living organisms have a very high information content, encoded in the DNA or genome of the organism. Even a simple bacterium has a specified complexity that makes it unimaginably unlikely to have come together by random events occurring over many times the entire multi-billion year life of the universe.
To summarize: a long sequence of coin tosses contains no more information than a shorter one, but a long sequence of amino acids in a protein contains more information than a shorter one. This distinction between random complexity and specified complexity is the key point developed in this scholarly book.
