The Design Inference: Eliminating Chance through Small Probabilities (Cambridge Studies in Probability, Induction and...

Features

• Cover Type: Paperback with 262 pages
• Published by: Cambridge University Press January 9, 2006
• Written in: English
• ISBN 10 Number: 0521678676
• ISBN 13 Number: 978-0521678674
• Book Dimensions: 9 x 6 x 0.5 inches
• Weighs: 12.6 ounces

Product Review
"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 difficultThe main argumentsare given in ordinary prose, then translated into symbolsDembski 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 amazing 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

Product Description
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.

Reader Reviews
This review is from: The Design Inference: Eliminating Chance through Small Probabilities (Cambridge Studies in Probability, Induction and Decision Theory) (Hardcover) 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.