The Design Inference
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5.0 • 1 Rating
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- $9.99
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- $9.99
Publisher Description
A landmark of the intelligent design movement, The Design Inference revolutionized our understanding of how we detect intelligent causation. Originally published twenty-five years ago, it has now been revised and expanded into a second edition that greatly sharpens its exploration of design inferences. This new edition tackles questions about design left unanswered by David Hume and Charles Darwin, navigating the intricate nexus of chance, probability, and design, and thereby offering a novel lens for understanding the world. Using modern concepts of probability and information, it exposes the inadequacy of undirected causes in scientific inquiry. It lays out how we infer design via events that are both improbable and specified. Amid controversial applications to biology, it makes a compelling case for intelligent design, challenging the prevalent neo-Darwinian evolutionary narrative. Dembski and Ewert have written a groundbreaking work that doesn't merely comment on contemporary scientific discourse but fundamentally transforms it.
Customer Reviews
Clear and Compelling
I found this book to be very well written and helpful for my own research and thinking looking at the statistical properties behind Artificial Intelligence. The way it links statistical hypothesis testing with description length is a huge contribution to the field of statistics, because as Fisher noted p values are often arbitrary. I especially enjoyed this book’s discussion of probabilistic resources, along with its interpretation among Bayesian and Frequentist perspectives.
My recommendation is to try and check any expectations about the controversy around Intelligent Design at the door when reading this book. I’m someone who is skeptical of Intelligent Design, yet nothing in this book was unreasonable or an overreach given the mechanisms proposed and described (in fact, metaphysical naturalists seem to be the ones overreaching in their failure to engage with arguments such as the ones presented here). As a result, this book is surprisingly uncontroversial and instead builds up its main idea from axiomatic probability theory.
As the book started I wished it would get more technical about the details, however all these details were eventually filled in as the book went on and most of my questions and objections were answered by the end. In retrospect, I appreciated this organization because someone unfamiliar with probability theory or information theory gets a more gentle introduction to these topics before the book goes deep.
In summary, this book is a big contribution to the field of statistics (in particular philosophy of statistics) and its main result should have a considerable impact in STEM fields for years to come.