1.
Nature and Art
In Book II of the Physics Aristotle remarks, “If the ship-building art
were in the wood, it would produce the same results by nature.” Aristotle is
here contrasting nature and art. Nature provides the raw materials (here
wood); art provides the means for fashioning those materials (here into a
ship). For Aristotle, art consists in the knowledge and skill to produce an
object and presupposes the imposition of form on the object from outside. On
the other hand, nature consists in capacities inherent in the physical
world--capacities that produce objects, as it were, internally and without
outside help. Thus in Book VII of the Metaphysics Aristotle writes,
“Art is a principle of movement in something other than the thing moved;
nature is a principle in the thing itself.” Consequently, Aristotle refers to
art as completing “what nature cannot bring to a finish.” Thomas Aquinas took
this idea and sacramentalized it into grace completing nature.
In Aristotle’s distinction between art and nature lies the central issue in
the debate over biological evolution. The central issue is not the
interpretation of Genesis, nor whether humans are descended from apes, nor
whether all organisms trace their lineage to a last common ancestor. Indeed,
where one comes down on these side issues is irrelevant to the central issue.
The central issue is whether nature has sufficient resources in herself to
generate all of biological diversity or whether in addition nature requires
art to complete what nature alone cannot bring to a finish. The Greek word
for art is techne, from which we get our word technology. The English
word most commonly used to capture what Aristotle means by art derives not
from the Greek but from the Latin. That word is, of course, design.
The central issue in the debate over biological evolution can therefore be
put as follows: Is nature complete in the sense of possessing all the resources
necessary to bring about the biological structures we see around us or does
nature also require some contribution of design to bring about those
structures? A typical reaction to this question is simply to observe that
biological systems are natural objects and then to pose the following
counter-question: What besides nature could conceivably have played an
essential role in the formation of biological systems? Although there has
been no dearth of answers to this counter-question (special creation,
vitalism, and orthogenesis come to mind), the answers given to date no longer
inspire confidence within much of the scientific community.
It is therefore important to understand that intelligent design (or ID as it
is increasingly being abbreviated) is not yet another answer to this
counter-question. To ask what besides nature could conceivably have played an
essential role in the formation of biological systems is to ask for an entity
with causal powers to produce objects that nature unassisted could not
produce. The problem is that any such entities are not open to direct
empirical investigation. Our knowledge of them can be at best indirect,
dependent on phenomena mediated through nature. But a designing intelligence
that mediates its action through nature has since the time of Darwin seemed
largely dispensable--certainly from science and now increasingly from common
life.
The strength of intelligent design as an intellectual project consists not in
presupposing a prepackaged conception of a designer and then determining how
the facts of science square with that conception. Rather, intelligent
design’s strength consists in starting with nature, exploring nature’s
limitations, and therewith determining where design fits in the scheme of
nature. Aristotle claimed that the art of ship-building is not in the wood
that constitutes the ship. Likewise intelligent design claims that the art of
life-building is not in the physical stuff that constitutes life. But
intelligent design does not stop there. Rather, the very methods that
establish nature’s limitations also establish that design is operating in
nature. Nor does intelligent design commit a god-of-the-gaps fallacy.
Intelligent design locates discontinuities in the causal structure of nature
that are inherently unbridgeable by natural causes. Such gaps are ontological
rather than epistemic, and thus offer no promise of being removed by closer
investigation of natural causes.
But why admit any gaps at all? Nature gives rise to human beings. Once human
beings are on the scene, they act as designing intelligences to produce
artifacts. But human beings are themselves natural. Art in Aristotle’s sense
is therefore at most once removed from nature: Nature produces embodied
rational agents like us, who in turn produce designed objects. To speak of
nature herself being designed or to speak of natural objects (like biological
systems) being designed seems therefore to commit a category mistake. To
state the problem in the language of evolution: Nature in her evolution
produces life, and some of those evolved forms of life produce designed
objects. Yet to place design prior to the evolved forms that produce design
is to misconceive design.
The problem with this objection is that it still fails to address nature’s limitations,
especially with regard to the emergence of biological systems. Does nature in
and of herself--unassisted and unsupplemented--have what it takes to produce
the diversity of life? To be sure, one can simply as a metaphysical
assumption suppose that nature can do all her own designing. Aristotle made
this assumption, and so did the ancient Stoics. For Aristotle, final causes
operated as a part of nature. Final causes expressed purposes inherent in
nature and were therefore capable of effecting design (biological designs in
particular). Thus in Book II of the Physics Aristotle writes of
purpose being present in both art and nature. But endowing nature with
purpose and therewith empowering nature to produce design is not an option
for most contemporary scientists. As Jacques Monod put it, “The cornerstone
of the scientific method is the postulate that nature is objective. In other
words, the systematic denial that ‘true’ knowledge can be got at by
interpreting phenomena in terms of final causes--that is to say, of
‘purpose’.”
Whence the removal of purpose and therewith design from nature? I lay the
blame with the mechanical philosophy that was prevalent at the birth to
modern science. Paradoxically, the very clockwork universe that the early
mechanical philosophers like Robert Boyle used to buttress design in nature
was in the end probably more responsible than anything for undermining design
in nature. The mechanical philosophy viewed the world as an assemblage of
material entities interacting by purely mechanical means. Boyle advocated the
mechanical philosophy because he saw it as refuting the immanent teleology of
Aristotle and the Stoics for whom design arose as a natural outworking of
natural forces. For Boyle this was idolatry, identifying the source of
creation not with God but with nature.
The mechanical philosophy offered a world operating by mechanical principles
and processes that could not be confused with God’s creative activity and yet
allowed such a world to be structured in ways that clearly indicated the
divine handiwork and therefore design. What’s more, the British natural
theologians always retained miracles as a mode of divine interaction that
could bypass mechanical processes. Over the subsequent centuries, however,
what remained was the mechanical philosophy and what dropped out was the need
to invoke miracles or God as designer. Henceforth, purely mechanical
processes could themselves do all the design work for which Aristotle and the
Stoics had required an immanent natural teleology and for which Boyle and the
British natural theologians required God.
2. Testing Nature’s Limits
The mechanical philosophy is still with us, though in place of particles and
force we now tend to think in terms of fields and energy. The mechanical philosophy
has bequeathed to us a view of nature in which natural processes operate
unsupplemented by any form of teleology, purpose, or design. Fortunately,
this view of nature is testable. To see this, I will need to describe some of
my own work on design detection (especially as laid out in my book The
Design Inference). Yet instead of merely recapitulating that work, I will
approach it through Murray Gell-Mann’s work on effective complexity and total
information.
Since the early 1990s Gell-Mann has been attempting to combine Shannon’s
statistical theory of information with Kolmogorov’s algorithmic theory of
information into a comprehensive theory of complexity and information for
science. Gell-Mann starts with the observation that the complexity that interests
us in practice is not pure randomness but patterned regularities that remain
once the effects of randomness have been factored out. Gell-Mann thus defines
“effective complexity” as the complexity inherent in these patterned
regularities. Moreover, he defines “total information” as the effective
complexity together with the complexity inherent in the effects of randomness
that were factored out. He then characterizes effective complexity
mathematically in terms of an algorithmic information measure that measures
the extent to which patterned regularities can be compressed into a minimal
representation (he calls such representations “schemata”). Moreover, he
characterizes the residual effects of randomness mathematically in terms of a
Shannon information measure that measures the extent to which random
deviations depart from the patterned regularities in question. Total
information thus becomes the sum of an algorithmic information measure and a
Shannon information measure.
Gell-Mann’s theory of effective complexity attempts to account for how
complex adaptive systems like us make sense out of a world that exhibits
regularities as well as random deviations from those regularities. Though
richly suggestive, applying Gell-Mann’s mathematical formalism in practice is
largely intractable since it requires taking conceptual schemata of patterned
regularities appropriate to some inquiry, mapping them onto a computational
data structure, and then seeing how such data structures can be reduced in
size while faithfully preserving the conceptual structures that map from
conceptual to computational space. Thus far Gell-Mann’s theory has resisted
detailed applications to real-world problems.
Why then do I consider it here? According to philosopher David Roche, design
theorists like me are all mixed up about information theory and complexity.
Thus Roche argues that the Darwinian mechanism is well able to account for
biological complexity once we are clear about the type of complexity that is
actually at issue in biology. The problem, according to Roche, is that design
theorists are using the wrong notion of complexity. What is the right notion?
Roche claims Gell-Mann’s concept of effective complexity is the right one for
biology.
But there is a problem with Gell-Mann’s approach to complexity. While
Gell-Mann’s approach is well-suited for describing how regularities of nature
that are subjected to random perturbations match our conceptual schemata, it
is not capable of handling contingencies in nature that are unaccountable by
any regularities but that happen all the same to match our conceptual
schemata. Such contingencies establish a design in nature that is not
reducible to nature. What are these contingencies that are unaccountable by
regularities but that nonetheless match our conceptual schemata? The
technical name for such contingencies is specified complexity.
Think of the signal that convinced the radio astronomers in the movie Contact
that they had found an extraterrestrial intelligence. The signal was a long
sequence of prime numbers. On account of its length the signal was complex
and could not be assimilated to any natural regularity. And yet on account of
its arithmetic properties it matched our conceptual schemata. The signal was
thus both complex and specified. What’s more, the combination of complexity
and specification convincingly pointed those astronomers to an
extraterrestrial intelligence. Design theorists contend that specified
complexity is a reliable indicator of design, is instantiated in certain
(though by no means all) biological structures, and lies beyond the remit of
nature to generate it.
If the previous remarks about complexity, specification, and information have
seemed unduly elliptical, it is because this is a complicated subject and the
details can quickly become overwhelming, especially in so short a talk as
this. Nonetheless, I do want to give some sense of why specified complexity
is the right instrument for identifying nature’s limitations. To say that
specified complexity lies beyond the remit of nature to generate it is not to
say that naturally occurring systems cannot exhibit specified complexity or
that natural processes cannot serve as a conduit for specified complexity.
Naturally occurring systems can exhibit specified complexity and nature
operating unassisted can take preexisting specified complexity and shuffle it
around. But that is not the point. The point is whether nature can generate
specified complexity in the sense of originating it when previously there was
none. Take, for instance, a[n Albrecht] Durer woodcut. It arose by
mechanically impressing an inked woodblock on paper. The Durer woodcut
exhibits specified complexity. But the mechanical application of ink to paper
via a woodblock does not account for that specified complexity in the
woodcut. The specified complexity in the woodcut must be referred back to the
specified complexity in the woodblock which in turn must be referred back to
the designing activity of Durer himself. Specified complexity’s causal chains
end not with nature but with a designing intelligence.
To place the burden of design detection on specified complexity remains
controversial. The philosophy of science community, wedded as it is to a
Bayesian approach to probabilities, is still not convinced that my account of
specified complexity is even coherent. The Darwinian community, convinced
that the Darwinian mechanism can do all the design work in biology, regards
specified complexity as an unexpected vindication of Darwinism. On the other hand,
mathematicians and statisticians have tended to be more generous with my work
on specified complexity and to regard it as an interesting contribution to
the study of randomness. Perhaps the best reception of my work has come from
engineers and the defense industry looking for ways to apply specified
complexity to pattern matching. The final verdict is not in. Indeed, the
discussion has barely begun. In my forthcoming book titled No Free Lunch
I respond at length to my critics (including Wesley Elsberry). Since I will
presumably have some time to respond to Wesley’s criticisms of my work
following his talk, I’ll leave off further discussion of specified
complexity’s merits.
3. Technological Evolution
I want next to focus on what insights into biological evolution a design
perspective offers. Here we are at a conference on interpreting evolution.
Suppose that specified complexity lies beyond the remit of natural causes to
generate it, and that specified complexity is a reliable empirical marker of
actual design, and that specified complexity is instantiated in actual
biological systems (huge suppositions for many of you). How then should we
interpret biological evolution?
Phillip Johnson has criticized Ohio State University zoologist Tim Berra for
likening Darwinian evolution to the technological evolution of the Corvette
automobile. Darwinian evolution is by definition undirected by any
intelligence whereas Corvette evolution is directed by an intelligence.
According to Johnson, there is a fundamental disanalogy between these two
types of evolution, and to use one to justify the other is invalid. Johnson
therefore refers to Berra’s conflation of Darwinian evolution and
technological evolution as Berra’s Blunder. I prefer instead to refer to it
as Berra’s Freudian Slip. Berra was quite right to compare biological
evolution to technological evolution. Biological evolution is indeed a form
of technological evolution. Berra’s mistake was in thinking that Darwinian
evolution is a form of technological evolution. It is not.
Darwinian evolution is a trial-and-error method for gradually improving
preexisting functions and for co-opting serendipitous functions. Within
Darwinian evolution natural selection supplies the trial and random variation
the error. Although trial and error plays a role in technological evolution,
trial and error is too myopic to serve as the powering force behind
technological evolution. The watchmaker behind technological evolution needs
to be far-seeing, not myopic and certainly not blind.
We now have extremely good information about the trends that technologies
follow in their evolution. Once designed systems are in place, operational,
and interacting (be they within an economy or ecosystem), technological
evolution tends to follow certain patterns. These patterns of evolution have
been extensively studied by Russian engineers and scientists, beginning
notably with the work of Genrich Altshuller. As Semyon Savransky remarks,
“Engineers in the former Soviet Union were responsible to spend eight hours
[a day] at their work place but often had nothing to do (their regular salary
did not depend on their effort, experience, or quantity and quality of work).
Many of them ... used this time to study patents.”
Altshuller, an engineer, studied more than 400,000 patents from across the
world to uncover patterns in technological evolution. Another Russian
engineer, I. V. Vikent’ev, studied all USSR patents (about a million at the
time) looking for patterns in technological evolution. The systematic study
of patents by Russian engineers and scientists created a new discipline, now
known under the acronym T-R-I-Z. TRIZ corresponds to a Russian phrase that in
English means “Theory of Inventive Problem Solving.” Although Russian
researchers have been actively investigating TRIZ for the last fifty years,
it has only made its mark in the West in the last decade. TRIZ as a
methodology for facilitating inventions and solving problems is increasingly
being employed in industry. On the other hand, its applications to biology
are only now becoming evident.
TRIZ is a vast topic, so in my few remaining minutes I will provide only the
barest sketch of this methodology as it relates to biology. TRIZ is concerned
with the improvement of existing designs and the emergence of novel designs.
I’ll call the one intraspecific technological evolution, the other
transpecific technological evolution. Although intraspecific technological
evolution can proceed by trial and error (as in the Darwinian mechanism), the
trial-and-error method is only suitable, as TRIZ expert Semyon Savransky
observes, for “simple, well-defined, routine closed problems.” Problems are
routine if all the critical steps leading to a solution are known. On the
other hand, a problem is nonroutine if at least one critical step leading to
a solution is unknown.
In response to environmental pressure (be it economic or ecological),
intraspecific technological evolution is frequently called on to solve
nonroutine problems. Environmental pressure pushes designed systems toward
what TRIZ proponents call “ideality.” A system is said to approach ideality
to the degree that it maximizes the system’s useful functions and minimizes
its harmful functions. In the Marxist spirit in which TRIZ was invented, TRIZ
seeks to overcome the contradictions that arise when improving one function
of a system leads to deficits in another function of the system. TRIZ seeks
to resolve these contradictions not so much by balancing advantages against
disadvantages, as in constrained optimization, but by novel win-win solutions
that maximize useful functions without (ideally) incurring harmful
side-effects. The great obstacle in the way of ideality is psychological
inertia, which artificially constricts a solution space rather than opening
it to undreamt of possibilities. Psychological inertia thinks, as it were,
inside a box. Ideality requires thinking outside the box.
TRIZ characterizes ideality in the following Zen-like terms (I quote from
Savransky):
* The ideal machine has no mass or volume but accomplishes the required work.
* The ideal method expends no energy or time but obtains the necessary effect
in a self-regulating manner.
* The ideal process is actually only the process result without the process
itself.
* The ideal substance is actually no substance (a vacuum), but whose function
is performed.
* The ideal technique occupies no space, has no weight, requires no labor or
maintenance, delivers benefit without harm, and “does it itself,” without any
additional energy, mechanisms, cost, or raw materials.
This Zen-like dwindling of a system’s substantiality to nothing while its
function progresses to perfection is to be sure an idealization that cannot
be realized in any concrete physical system. Nonetheless, this idealization
serves as a useful regulative principle for designed systems. Certainly,
ideality’s best instantiation is found in biology (according to Genrich
Altshuller, biology has given us the best of all patent libraries). Among
human artifacts ideality’s best instantiation is perhaps found in computers.
Whether Moore’s law will continue to obtain and push computers closer to
ideality than biological systems (especially in regard to the human brain) is
very much a matter of debate at this time.
According to TRIZ, intraspecific evolution gives way to transpecific
evolution when a given technology has been pushed as close to ideality as
possible and when new pressures from the environment require new technologies
with new functions. When novel technological systems emerge, as far as
possible they take advantage of and incorporate preexisting technologies.
What’s more, novel systems tend to emerge suddenly. Once a novel system has
emerged, the pressure is on to achieve ideality. A system that approximates
ideality will persist for long stretches of time provided its environmental
niche is undisturbed. Stasis is therefore part of TRIZ’s evolutionary scheme.
But so is extinction: When environmental pressures become too great,
antiquated systems either give way to novel systems or simply disappear
without any system taking their place. Unlike emergence, which is sudden,
extinction can be sudden or gradual (thus a new technology may gradually
displace an old one or eliminate it all at once). Finally, good ideas get reused
and reinvented. Technological evolution therefore includes convergent
evolution. Moreover, it readily accommodates homologies (similar structures
used for different purposes) as well as analogies (different structures used
for similar purposes).
Sudden innovation, convergence to ideality, and extinction are all part of
TRIZ’s evolutionary scheme. Now where have we seen that scheme before? The
scheme is non-Darwinian. Nor can the Darwinian scheme be easily modified to
accommodate it. For instance, Robert Wright’s addition of game theory to
selection and variation is insufficient to account for technological
innovation--at best game-theoretic constraints provide a necessary condition
for technological innovation. TRIZ’s evolutionary scheme fits quite nicely
with Eldredge and Gould’s model of punctuated equilibria. Leaving aside their
model’s mechanism of evolutionary change and innovation, the patterns of
evolution described by TRIZ and the Eldredge-Gould model are quite similar.
Perhaps the one discrepancy is that the Eldredge-Gould model does not make
explicit the convergence to ideality. From the vantage of technological
evolution, the speed of convergence to ideality reflects the perspicacity of
the designing intelligence responsible for technological improvement. In the
limiting case, therefore, a designing intelligence produces technological
systems that are as close to ideality as possible from the start. Although
suboptimality of design remains an issue in biological evolution, aspects of
biological designs seem indeed to approach ideality. For instance, the
miniaturization of molecular machines in the cell seems to approach the
physico-chemical limits of matter.
In conclusion, Aristotle’s distinction between nature and art remains very
much a live issue for the natural sciences. In particular, at the heart of
the current debate over intelligent design is whether biological systems
exhibit some feature that cannot be ascribed to nature as such but in
addition requires art or design to complete what, as Aristotle put it,
“nature cannot bring to a finish.” Moreover, if design theorists are correct
in arguing that specified complexity lies beyond the remit of natural causes
to generate it, that specified complexity is a reliable empirical marker of actual
design, and that specified complexity is instantiated in actual biological
systems; then the way is open for a massive reinterpretation of biological
evolution. In that case, biological evolution becomes a form of technological
evolution. What’s more, thanks to TRIZ, a ready-made theory of technological
evolution is already in place to interpret biological evolution. Biology
confirms the patterns of technological evolution outlined by TRIZ.
Significantly, these patterns are non-Darwinian.
Reference Notes
The quotes from Aristotle are taken from Jonathan Barnes, ed., The
Complete Works of Aristotle (Princeton: Princeton University Press,
1984). For Internet information on TRIZ, start with http://www.triz.org and http://www.triz-journal.com. The
citations to Savransky and Altshuller are taken respectively from Semyon
Savransky, Engineering of Creativity: Introduction to TRIZ Methodology of
Inventive Problem Solving (Boca Raton, Fl.: CRC Press, 2000) and Genrich
Altshuller, The Innovation Algorithm: TRIZ, Systematic Innovation and
Technical Creativity (Worcester, Mass.: Technical Innovation Center,
1999).
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