Paul Nelson wrote:
[...]
FM> An interesting "riddle" was also given by Wesley with
FM> his "algorithm room".
FM> http://inia.cls.org/~welsberr/ae/dembski_wa.html
PN>Right. Wesley and I have talked about this in
PN>private correspondence. Ask Wesley if he knows
PN>of any evolutionary algorithm whose causal
PN>history (as lines of code) does not implicate at
PN>least one intelligent agent.
By Dembski's definition of the phrase "evolutionary algorithm",
natural selection itself would be a candidate.
Dembski used "the solution is infused by the intelligence
that went into the program, compiler, OS, computer, etc."
excuse at the 1997 NTSE in his discussion period. It
doesn't wash, as my argument in
<http://inia.cls.org/~welsberr/zgists/wre/papers/antiec.html>
shows.
PN>Put another way, evolutionary algorithms are proxy agents.
PN>If you pursue the causal story, you'll find the action of a
PN>designer somewhere down the road.
[...]
I could develop the following at greater length as an article
for "Origins and Design". How about it? Or do you have a
recommendation on some other venue for eventual publication?
Zygon? First Things? The Southern Journal of Philosophy?
What Does "Intelligent Agency by Proxy" Do For the Design Inference?
by Wesley R. Elsberry
William A. Dembski wrote "The Design Inference" as his
technical explication of the logic and methods of inferring
that an event must be explained as being due to design. In
other essays aimed at less technically inclined audiences (and
the book, "Intelligent Design", which collects some of those
essays), Dembski has also written about making design
inferences. There are certain aspects of Dembski's popular
writings which appear to be at odds with, or at least
unsupported by, the technical explication of "The Design
Inference".
[Quote]
Thus, to claim that laws, even radically new ones, can produce
specified complexity is in my view to commit a category
mistake. It is to attribute to laws something they are
intrinsically incapable of delivering-indeed, all our evidence
points to intelligence as the sole source for specified
complexity. Even so, in arguing that evolutionary algorithms
cannot generate specified complexity and in noting that
specified complexity is reliably correlated with intelligence,
I have not refuted Darwinism or denied the capacity of
evolutionary algorithms to solve interesting problems. In the
case of Darwinism, what I have established is that the
Darwinian mechanism cannot generate actual specified
complexity. What I have not established is that living things
exhibit actual specified complexity. That is a separate
question.
Does Davies's original problem of finding radically new laws
to generate specified complexity thus turn into the slightly
modified problem of finding find radically new laws that
generate apparent-but not actual-specified complexity in
nature? If so, then the scientific community faces a logically
prior question, namely, whether nature exhibits actual
specified complexity. Only after we have confirmed that nature
does not exhibit actual specified complexity can it be safe to
dispense with design and focus all our attentions on natural
laws and how they might explain the appearance of specified
complexity in nature.
[End Quote - WA Dembski,
<http://inia.cls.org/~welsberr/ae/dembski_wa/19990913_explaining_csi.html>]
In TDI, Dembski claims that we can examine the properties of
an event and classify it as being due to "regularity",
"chance", or "design". We need only the event itself and some
side information by which a specification may be formed.
Under Dembski's DI, what we do not need information about is
the cause of the event. This is important to Dembski's
argument because Dembski wants us to conclude "design" for an
event and then infer "intelligent agency" in cases where we
have no information about the "intelligent agent" which may
have caused the event in question.
In Dembski's examples of TDI, it is clear that known causal
stories are treated differently. They are not submitted to
his Explanatory Filter as possible "regularity" or "chance"
hypotheses. That Caputo cheated is not treated as either
"regularity" or "chance". Plagiary is not treated as either
"regularity" or "chance". DNA identification is not treated
as either "regularity" or "chance". Mendel falsifying data is
not treated as either "regularity" or "chance". These causal
stories instead are treated as the basis for "specifications"
and utilized in classifying an event as "due to design".
But in "Explaining Specified Complexity", Dembski does treat a
known causal story as either "regularity" or "chance". The
causal story in question is that an evolutionary algorithm
yields a specified result in a small number of tries out of a
large problem space. Here, Dembski tells us that the
complexity of the result (found by reference to a "chance"
hypothesis") is apparently large but actually zero, because
the probability of the result <i>given its known cause</i> is
1.
As pointed out above, Dembski's TDI does not condone plugging
in known causes in as "regularity" or "chance" hypotheses. At
best, one might plug in a hypothesized cause that is identical
to an actual cause. After all, some things <i>are</i> due to
regularity and chance. But let's consider what follows from
this change in operation between TDI and "Explaining Specified
Complexity".
We have two events, each yielding a solution to a 100-city
tour of the Travelling Salesman Problem. (I select this one
as an example because it has well-known characteristics and I
have been using it since 1997.) In one event, we know that a
human agent has toiled long and hard to produce the solution.
In the other case, a genetic algorithm was fed the city
coordinate data and spit out the same solution some time
later. We will now apply the Design Inference from TDI and
the Design Inference as modified in "Explaining Specified
Complexity".
For TDI_TDI, the known causal stories are irrelevant. Thus,
both events are treated identically, which is to say that our
speculations concerning how these events occurred may be the
basis for specifications, but otherwise do not impinge upon
our analysis. We eliminate "regularity", since these are not
high probability events. We eliminate chance, because these
are not simply intermediate probability events. We conclude
that the events are due to "design" because they are both
"small probability" (and in fact meet Dembski's universal
small probability bound) and are "specified" as the shortest
closed loop path that visits each city once. Both events are
classed as having "specified complexity".
This is not the case for TDI_ESC. Now, there is an asymmetry
in how we treat the two events based upon our knowledge of the
causal stories. For the solution given by the human, we again
decline to utilize our knowledge of causation, and things
proceed as for TDI_TDI, and we find the solution is due to
"design". Not so for the solution produced by GA. There are,
in fact, two possible alternate ways in which this event may
be processed which deny placing it in the "due to design" bin.
The one explicated in "Explaining Specified Complexity" goes
like this. First, regularity is eliminated; the event is not
of high probability. Second, we consider chance hypotheses
and find our complexity estimate thereby. We submit as a
chance hypothesis the known causal story: the result was
obtained by operation of a genetic algorithm. Unsurprisingly,
when we know that an event is due to a particular cause and
we use that cause as a "chance" hypothesis, we find that the
event is "due to chance". And because we base our complexity
measure upon the relevant chance hypothesis, we find that
the probability of the event given our "chance" hypothesis
is high, and thus the complexity is very low indeed.
The second possible way to eliminate the event yielded by
genetic algorithm is to treat the operation of the genetic
algorithm as a regularity. In this case, we again use our
knowledge that the event was caused by a genetic algorithm.
We note that genetic algorithms are capable of solving
problems of this apparent complexity, and class the solution
as being due to the regularity of solution by genetic
algorithm. Again, our classification is unsurprising, since
we applied our known causal story to a decision node in the
Explanatory Filter, we also find that our known causal story
explains the event.
In either of the above ways of avoiding making a successful
design inference for the solution produced by genetic
algorithm, we apply knowledge of the cause of the event
differently from when we know that the cause is an intelligent
agent. In the case where an intelligent agent is known to
act, we are told that the event represents "actual specified
complexity". In the case where an algorithm is known to have
produced the event, we are told that the event represents
"apparent specified complexity". Note that "apparent
specified complexity" is established only because we have
knowledge of the causal process and use it differently from
the analytic method given in TDI.
To clarify why these cases indicate problems for making Design
Inferences, consider an event where we are shown a solution to
a 100-city TSP, but we are <b>not</b> given any information as
to the causal story. We do not know whether an intelligent
agent or some algorithm worked out this solution; we merely
have the solution and our knowledge of the TSP problem in
general. According to the procedures and logic given in TDI,
we can make a reliable inference of "design" given just that
information. And as indicated before, this event when
analyzed according to TDI_TDI is classified as "due to
design". We now have a problem: The event is "due to design",
but it may not reliably mark the work of an intelligent agent
in producing it. This is a challenge to the claim that TDI
gives us a reliable method of inferring the action of
intelligent agents. Because the same event could have either
"apparent specified complexity" or "actual specified
complexity", we find ourselves exactly where we were before
having used TDI. The mere fact that an event has "specified
complexity" does not enable us to reliably infer the action
of an intelligent agent in producing it.
One way of approaching this challenge is to repudiate the
claim that there is any such split between "apparent specified
complexity" and "actual specified complexity". This would
preserve the concept of "specified complexity" as having some
bearing upon marking the action of intelligent agency, rather
than simply being a complicated piece of rhetoric whose
content is solely a long-winded way of begging the question.
Since the only effects of "apparent" vs. "actual" specified
complexity categories are to cast doubt upon the logical
framework and methods of the Design Inference, repudiating it
seems the clear way to proceed. But then there is still the
problem that human and algorithm may produce identical events
that are tagged as having "specified complexity".
When "apparent" vs. "actual" specified complexity is
repudiated, the residual problem may then be approached by
claiming that whenever an algorithm is the cause of an event
having the property of "specified complexity", that we may
infer that an intelligent agency designed and implemented the
algorithm, and that the production of events by such
algorithms is in each case to be considered "intelligent
agency by proxy" (IABP). [This approach, including the
repudiation of "apparent specified complexity", was taken by
Paul Nelson in personal correspondence from October, 1999.]
Thus, whenever "design" is found, we are assured that an
intelligent agent operated, either to produce the event
proximally, or to produce the process by which the event
occurred ultimately.
There are further problems that ensue from use of IABP, but
fortunately for the Design Inference these turn out to be
relatively simple inconsistencies between some of Dembski's
claims outside of TDI and those covered within TDI. In other
words, retaining the "apparent" vs. "actual" specified
complexity distinction entered by Dembski logically
invalidates the Design Inference (it is somewhat ironic for an
author to vitiate his own work), while dumping it and adopting
IABP yields a revised form of TDI which is still arguable.
Now, I will consider what adoption of IABP implies for the
Design Inference.
First, IABP invalidates Dembski's claim in "Intelligent
Design" that "functions, algorithms, and natural law" cannot
produce specified complexity aka "complex specified
information". Instead, functions, algorithms, and natural
laws which are produced by intelligent agents and which act as
proxies for those agents also have the ability to produce
events with specified complexity.
Second, IABP means that the method of the Design Inference
cannot distinguish between direct proximal action of an
intelligent agent in producing an event and indirect action
via proxy one or an infinite number of steps removed. Once
a process has been made by an intelligent agent as a proxy,
whatever events it might produce henceforth would then be
capable of yielding events with specified complexity.
There is no basis in the Design Inference for distinguishing
between two events, one produced directly by an intelligent
agent, and an identical one produced by that agent's proxy.
Consider the TSP example given above. A human can produce
a genetic algorithm that solves TSP problems. The same
human can work TSP problems even as his algorithm is employed
doing the same thing. As long as each is working properly,
they may both produce solutions (or equivalently close
approximate solutions) to TSP problems. The Design Inference
can only detect "specified complexity", and thus cannot tell
us whether any particular TSP solution was produced by the
human or by his algorithmic proxy.
Third, IABP undermines Dembski's position taken in
"Intelligent Design" that attributing processes rather than
contrivances to the intelligent agency of God is an error.
Because one can examine a contrivance as an event via TDI, but
the results are ambiguous with respect to whether the
contrivance's specified complexity is due to God's direct
intervention in producing the contrivance or due to God's
indirect causation through one or an infinite number of steps
removed via a function, algorithm, or natural law set up as a
proxy process, one cannot distinguish via TDI whether God acts
directly or not for any particular contrivance examined.
Fourth, IABP implies that the strongest theological claim that
can be predicated upon the Design Inference is a version of
Deism wherein the Deist God undertakes creating a complete set
of proxy functions, algorithms, and natural laws which result
in the universe and life as we know it. Specifically, the
Design Inference is incapable of asserting a direct
intervention of God in forming irreducibly complex biological
systems. Displacing a hypothesized instance of the action of
natural selection in adaptation is conceptually beyond the
reach of the Design Inference or "specified complexity". At
best, on the basis of the Design Inference alone under IABP it
can be claimed that the concept and implementation of natural
selection is due to God, not that it was not operative as a
proxy for God.
In conclusion, the principle of "intelligent agency by proxy"
helps save the Design Inference from the logical collapse
necessitated by adoption of the distinction between "apparent
specified complexity" and "actual specified complexity", but
imposes certain costs of its own. In particular, several of
the auxiliary statements about the Design Inference made by
William Dembski in his popular writings would have to be set
aside. These include the claim that "functions, algorithms,
and natural law" cannot produce events with specified
complexity, and that identification of specified complexity
for biological systems implies that natural selection was not
operative. IABP and the Design Inference can be used
theologically as an argument for the existence of a God with
Deist properties. Stronger arguments than that will need to
be justified independently.
Wesley
<http://inia.cls.org/~welsberr/ae/dembski_wa/wre_id_proxy.txt>
<http://inia.cls.org/~welsberr/ae/dembski_wa.html>
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