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Applying Bayes’ Theorem to Biological Design

In a post yesterday, I asked how we can apply the mathematical tool of Bayes’ Theorem to the case for biological design. See, “A Bayesian Approach to Intelligent Design.”  I propose that we construct the argument as follows: Given the hypothesis that living systems are the result of design, it is not extremely unlikely that biological systems would be rich in digitally encoded information content and irreducibly complex machinery (given that we know from experience with human agents that they often generate information and produce irreducibly complex contraptions). On the other hand, it is incredibly unlikely that such information-rich systems and irreducibly complex machinery could have arisen by an unguided natural process involving chance and physical necessity.

Bari Weiss Knows What ID Scientists Already Knew

New York Times opinion journalist Bari Weiss submitted her very public resignation today. It’s a must read, and it will remind you at once of the world scientists in the intelligent design community have long occupied.  “Forays into Wrongthink” Some excerpts: new consensus has emerged in the press, but perhaps especially at this paper: that truth isn’t a process of collective discovery, but an orthodoxy already known to an enlightened few whose job is to inform everyone else….I was always taught that journalists were charged with writing the first rough draft of history. Now, history itself is one more ephemeral thing molded to fit the needs of a predetermined narrative.My own forays into Wrongthink have made me the subject of constant

Welcome Back, Jonathan McLatchie, and Congratulations!

It’s gratifying to reintroduce a longtime contributor, Jonathan McLatchie! He is currently writing a short series on Bayes’ Theorem and intelligent design, so be sure to follow his writing here. Many Evolution News readers will already know Jonathan, who has written for us for years under the name “Jonathan M.” As of January 2020, Jonathan M. is now Dr. Jonathan McLatchie. He earned a PhD in evolutionary biology from the Biosciences Institute at Newcastle University in the UK. Dr. McLatchie is now Assistant Professor of Biology at Sattler College in Boston, MA. He’s pictured above (r.) in a debate, back in January, with Oxford University physical chemist Peter Atkins (l.). Welcome home, and congratulations! By the way, Dr. McLatchie's debate with Dr. Atkins is on YouTube,

A Bayesian Approach to Intelligent Design

I have recently been interested in the field of epistemology, which is the branch of philosophy that deals with how we reliably form beliefs, or how we acquire knowledge. In particular, I have been interested in how we can quantify the strength of a particular piece of evidence, and how a cumulative case, involving many different lines of evidence, may be modeled mathematically. I have come to think of evidence in Bayesian terms and this has in turn impacted the way I think about the biological arguments for intelligent design. What is Bayes’ Theorem? Named for the 18th-century English Presbyterian minister Thomas Bayes, Bayes’ Theorem is a mathematical tool for modelling our evaluation of evidences to appropriately apportion the confidence in our conclusions to

What Can and Can’t Darwin’s Algorithm Compute?

Editor’s note: We are delighted to welcome a new contributor, Roman V. Yampolskiy, Associate Professor in the department of Computer Engineering and Computer Science at the Speed School of Engineering, University of Louisville. In a new series, Dr. Yampolskiy asks: “What Can and Can’t Darwin’s Algorithm Compute?” In 1859 Charles Darwin1 and many scholars before him2, 3 have proposed theories to explain the origins of complex life forms via natural selection and modification. Scientific theories are algorithms which, given starting conditions as input, make statistically accurate predictions of the future state of the system. For example, computer simulations of continental drift give us positions of continents at some time t. I have emphasized the importance of such
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