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

But there could be discontinuity at some points, not necessarily "radical"
discontinuity, producing the same branching tree pattern.

On Sun, Aug 10, 2008 at 3:37 PM, Stephen Matheson <smatheso@calvin.edu>wrote:

> It is my strong impression that the "special" part of "special creation"
> is nothing other than discontinuity. Why else would anyone need to add
> "special" to "creation" when talking of the works of God that are affirmed
> by all Christians? There is, by the way, no simulation that would negate
> "progressive creation in which God gradually creates new species using
> existing species as a base," since that is evolution by definition.
>
> I find the very nature of this discussion to be absurd. Musing about the
> distinctions between common descent and "continuous creation" is like
> discussing whether your lunch was provided by God if you bought the corn
> from a farmer.
>
> Steve Matheson
>
>
> >>> "David Opderbeck" <dopderbeck@gmail.com> 08/10/08 3:21 PM >>>
>
> 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
>

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