Conservation of Information in Search - Measuring the Cost of Success

William A. Dembski, Senior Member, IEEE, and Robert J. Marks II, Fellow, IEEE wrote an article titled: Conservation of Information in Search: Measuring the Cost of Success for ''IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems AND Humans'', Vol. 39, NO. 5, September 2009 (the article can be found here) In his blog Uncommon Descent, W. Dembski states that this peer-reviewed article is about Intelligent Design: Our critics will immediately say that this really isn't a pro-ID article but that it's about something else (I've seen this line now for over a decade once work on ID started encroaching into peer-review territory). Before you believe this, have a look at the article. In it we critique, for instance, Richard Dawkins METHINKS*IT*IS*LIKE*A*WEASEL (p. 1055). Question: When Dawkins introduced this example, was he arguing pro-Darwinism? Yes he was. In critiquing his example and arguing that information is not created by unguided evolutionary processes, we are indeed making an argument that supports ID.

As is frequently the case when creationists/cdesign proponentsists purport to analogize biological facts from information theory, active information (the use of which Dembski claims renders the weasel algorithm an incorrect analogy) is never concretely defined. It appears to simply mean the fitness function, which any genetic algorithm has (or there would be no point in creating it) and which, contra Dembski's implied claim, says nothing about a designer &mdash; the fitness function in a biological environment is simply the effect of the combination of selective pressures applied by environmental conditions at any given time. It may also mean the source of variation, which in Dawkins' algorithm is expressly identified as the chance of mutating any given letter and which in biology is held to be the combined effect of differing mutations and selective pressures experienced by different populations.

This article takes a closer look at two examples of chapter III: Examples of Active Information in Search (pp. 1055-1056):
 * E. Partitioned Search
 * F. Random Mutation

III. Examples of Active Information in Search
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