Re: [asa] Proof That Common Descent is NOT Begging the Question

Would it? Inheritedness in the same order as predicted by evolution?
So why would special creation have the supposedly disjoint set of
features that are connected was what would be expected through
inherited traits?

On Aug 10, 2008, at 9:22 AM, David Opderbeck wrote:

> Of course, a strong ID / progressive creationist would say that the
> algorithm would predict the same structure under their model, so the
> ultimate conclusion of which model finally is correct remains
> question begging.
>
> On Fri, Aug 8, 2008 at 7:50 PM, Rich Blinne <rich.blinne@gmail.com>
> wrote:
> One of the complaints made by strong ID is that common descent is
> assumed and then we find it and that continuous creation is just as
> plausible. A novel computer algorithm is presented in this week's
> PNAS shows that an unbiased search for structure shows otherwise.
>
> http://www.pnas.org/content/105/31/10687.full
>
> The discovery of structural form
>
> Algorithms for finding structure in data have become increasingly
> important both as tools for scientific data analysis and as models
> of human learning, yet they suffer from a critical limitation.
> Scientists discover qualitatively new forms of structure in observed
> data: For instance, Linnaeus recognized the hierarchical
> organization of biological species, and Mendeleev recognized the
> periodic structure of the chemical elements. Analogous insights play
> a pivotal role in cognitive development: Children discover that
> object category labels can be organized into hierarchies, friendship
> networks are organized into cliques, and comparative relations
> (e.g., "bigger than" or "better than") respect a transitive order.
> Standard algorithms, however, can only learn structures of a single
> form that must be specified in advance: For instance, algorithms for
> hierarchical clustering create tree structures, whereas algorithms
> for dimensionality-reduction create low-dimensional spaces. Here, we
> present a computational model that learns structures of many
> different forms and that discovers which form is best for a given
> dataset. The model makes probabilistic inferences over a space of
> graph grammars representing trees, linear orders, multidimensional
> spaces, rings, dominance hierarchies, cliques, and other forms and
> successfully discovers the underlying structure of a variety of
> physical, biological, and social domains. Our approach brings
> structure learning methods closer to human abilities and may lead to
> a deeper computational understanding of cognitive development.
>
> With respect to biological relationships the paper notes:
>
> For centuries, the natural representation for biological species was
> held to be the "great chain of being," a linear structure in which
> every living thing found a place according to its degree of
> perfection (16). In 1735, Linnaeus famously proposed that
> relationships between plant and animal species are best captured by
> a tree structure, setting the agenda for all biological
> classification since.
>
> In Figure 3 of the paper (http://www.pnas.org/content/105/31/10687/F3.large.jpg
> ) the computer program looked at the following structures:
>
> Structures learned from biological features (A), Supreme Court votes
> (B), judgments of the similarity between pure color wavelengths (C),
> Euclidean distances between faces represented as pixel vectors (D),
> and distances between world cities (E). For A–C, the edge lengths
> represent maximum a posteriori edge lengths under our generative
> model.
>
>
> The Supreme Court decision produced the left to right linear
> structure one would expect by doing a political analysis of the
> court. Did the computer program produce the tree structure predicted
> by Darwin when looking at biological features? Yes, it did! The
> caption to figure 5 (http://www.pnas.org/content/105/31/10687/F5.large.jpg
> ) explains this in more detail:
>
> Developmental changes as more data are observed for a fixed set of
> objects. After observing only five features of each animal species,
> the model chooses a partition, or a set of clusters. As the number
> of observed features grows from 5 to 20, the model makes a
> qualitative shift between a partition and a tree. As the number of
> features grows even further, the tree becomes increasingly complex,
> with subtrees that correspond more closely to adult taxonomic
> intuitions: For instance, the canines (dog, wolf) split off from the
> other carnivorous land mammals; the songbirds (robin, finch), flying
> birds (robin, finch, eagle), and walking birds (chicken, ostrich)
> form distinct subcategories; and the flying insects (butterfly, bee)
> and walking insects (ant, cockroach) form distinct subcategories.
> More information about these simulations can be found in SI Appendix.
>
> This is a computer program so it shows that the structure assumed by
> Darwin in fact is the structure that comes naturally out of the data
> itself. Thus, evolutionary biology is not begging the question after
> all but is just following good old fashioned human pattern matching.
>
> Rich Blinne
> Member ASA
>
>
>
>
>
> --
> David W. Opderbeck
> Associate Professor of Law
> Seton Hall University Law School
> Gibbons Institute of Law, Science & Technology

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Received on Sun Aug 10 14:21:36 2008