- Description
- About the Authors
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Science has made great strides in modeling space, time, mass and energy. Yet little attention has been paid to the precise representation of the information ubiquitous in nature.
Introduction to Evolutionary Informatics fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits. Built on the foundation of a series of peer-reviewed papers published by the authors, the book is written at a level easily understandable to readers with knowledge of rudimentary high school math. Those seeking a quick first read or those not interested in mathematical detail can skip marked sections in the monograph and still experience the impact of this new and exciting model of nature's information.
This book is written for enthusiasts in science, engineering and mathematics interested in understanding the essential role of information in closely examined evolution theory.
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Robert J Marks II is Distinguished Professor of Engineering in the Department of Engineering at Baylor University, USA. Marks's professional awards include a NASA Tech Brief Award and a best paper award from the American Brachytherapy Society for prostate cancer research. He is Fellow of both IEEE and The Optical Society of America. His consulting activities include: Microsoft Corporation, DARPA, and Boeing Computer Services.
William A Dembski is Senior Research Scientist at the Evolutionary Informatics Lab in McGregor, Texas; and also an entrepreneur developing educational websites and software. He holds a BA in Psychology, MS in Statistics, PhD in Philosophy, and a PhD in Mathematics (awarded in 1988 by the University of Chicago, Chicago, Illinois, USA), and an MDiv degree from Princeton Theological Seminary (1996, New Jersey, USA).
Winston Ewert is currently a Software Engineer in Vancouver, Canada. He is a Senior Research Scientist at the Evolutionary Informatics Lab. Ewert holds a PhD from Baylor University, Waco, Texas, USA. He has written a number of papers relating to search, information, and complexity including studies of computer models purporting to describe Darwinian evolution and developing information theoretic models to measure specified complexity.
Cover Type: Hardcover, Paperback
Page Count: 332
Year Published: 2017