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

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<http://www.pnas.org/content/105/31/10687.full#ref-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*<http://www.pnas.org/cgi/data/0802631105/DCSupplemental/Appendix_PDF>.
>>
>>
> 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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