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

Why does "special creation" have to be radically discontinuous? The big
question is mechanism, and the raw fact of a branching tree structure
doesn't address that. Mechanism is an inference, and it's at the level of
inference that the issue of question begging arises.

I think you're right if the argument is that this kind of algorithm shows
that a theory of special creation / design in which new species are created
from scratch in a short period of time is not viable. But I don't see how
it negates progressive creation in which God gradually creates new species
using existing species as a base. Whether or not God did that is another
question, but I don't see how this kind of simulation rules it out.

On Sun, Aug 10, 2008 at 2:21 PM, Rich Blinne <rich.blinne@gmail.com> wrote:

> 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<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
>
>
>

-- 
David W. Opderbeck
Associate Professor of Law
Seton Hall University Law School
Gibbons Institute of Law, Science & Technology
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