Evolutionary
theory is still slowly winning its acceptance.
Such is the case with any theory that opposes traditional thought long
engraved in the minds of many people.
And not only out of tradition do we refuse to accept new ideas. When these ideas contradict what we want to
believe, they are often denied. It took
over a century after Copernicus proposed the idea in 1543 before the world
accepted that the Earth is not the center of the universe but revolves around
the sun. This is an example of how naivete in human knowledge seems to produce
a sense of egocentrism. Likewise,
accepting evolutionary theory means accepting, among countless other
“unsettling” things, that humans came from lowly organisms, were not created by
a divine hand, and may have never even come into existence had chance led
evolution in a different path. These
implications were precisely the reason why Darwin was hesitant to publish the Origin of Species, until 1859 when
inspired by Wallace. However, time has passed and neo-Darwinism, Darwin’s
theory in combination with growing knowledge in the genetics field, has won
general acceptance as modern genetics have filled holes Darwin left on the topic
of inheritance. Yet opposition to the
theory has by no means disappeared.
Among the most prominent is the argument from intelligent design: “Of course the world was created, how else
could it be so complex?” In this
argument we see the main reason why acceptance of neo-Darwinism, as well
biology as a whole, as an axiomatic and pure science (like physics) is somewhat
hard to swallow. In its intricacy,
evolutionary theory is hard to visualize.
Acceptance is therefore hampered by disbelief and sometimes fear that
the complexity we see in our world today (and how we came to exist) can be
broken down into many simple, mathematical steps that follow logically from one
to the next. While there may be
uncertainty of evolutionary biology as an axiomatic system, it remains a valid
and useful science.
So
what does it take to be axiomatic?
There must first be accepted statements that need no justification
(axioms). Next, there must be agreement
on how and when one axiom follows logically from another (Greenberg). And then, by deduction, results can be
proven. Darwin was indeed aiming for an
axiomatic system. In the Origin, he provides a deductive argument
for natural selection (also loosely known today as population genetics), the
heart of the theory of evolution, and a term which he himself often uses
interchangeably with the theory itself (Ruse, 1988). From his argument we can pull the following axioms:
Darwinian
Axiom 1: There is variation among organisms.
Darwinian
Axiom 2: There is a struggle for existence.
In
Darwin’s own words, these statements “cannot be disputed,” the first because of
mere observation of living things under different conditions (temporally or
spatially) and the second due to a geometric increase in individuals with only
an arithmetic increase in resources, an idea borrowed from Malthus (1798). Thus, there must be some variation rendering
advantageous to an organism’s welfare, in that the organism somehow gains a
better chance of survival over another, and we can deduce a proposition:
Proposition 1
(Natural Selection): There is preservation of selected
individuals
However,
Darwin’s theory for natural selection may not be so simple. As Dennet (1995) points out, Darwin himself
described the entire Origin of Species as
“one long argument,” using two types of demonstrations. One is the “logical demonstration that a sort of process would necessarily have a
certain sort of outcome,” and the second is the “empirical demonstration that
the requisite conditions for that sort of process had in fact been met in
nature”. The former he supports with
“imaginary instances” to show how the conditions might account for the effects,
and the latter with many detailed examples that the conditions have indeed been
met over and over again in nature. In
support of Darwin, with his long lists of evidence he was unable to find a
model, or an interpretation of his system, for which his proposition of natural
selection failed to hold, and therefore the theory could not be disproved. However, challengers of evolution don’t find
this inability to disprove sufficient for proof.
Perhaps
the biggest misunderstanding of Darwin’s theory, as Dennet (1995) argues, is
that some forget that “algorithms” such as his don’t have points or purposes,
and therefore believe his theory of evolution is a procedure for producing
humans. Again, egocentrism blurs the
view. Considering evolution as a
process with purpose turns natural selection into an absurdly complex formula,
where the present state of affairs is the “solution.” This simply contradicts the heart of natural selection, where the
present state of affairs is the product of occurances without intention.
Even if Darwin’s theory of evolution by the
means of natural selection is accepted as logical and axiomatic, current
evolutionary theory, the foundation of modern biology, is not so simple. Now at the core of evolutionary theory is
the modernized natural selection, or “population genetics.” Evolutionists have therefore developed a
claim about genes in populations, the Hardy-Weinberg law, which requires the
following axiom:
Neo-Darwinian
Axiom 1: Genes are passed on from one individual to
another by processes derived from basic Mendelian laws.
And from with this axiom the Hardy-Weinberg law of
equilibrium can be deduced:
Proposition
2: In the absence of disruptive forces, the genetic make-up of
a population is at equilibrium.
Such forces of disruption
include selection and sampling effects due to finite numbers (Ruse 1988). At first consideration, such a proposition
may seem restrictive. What good is a
theory that can only predict the genetic make-up of a population “in the
absence of disruptive forces”? How is
this applicable to real life? This is
where the complexity comes in. Such a
theory is used by evolutionary biologists, not to know that when they do find
that magical population free from any environmental pressure that the
population is at equilibrium. Rather,
the theories are used as foundations to build from in order to determine the
forces at hand. This point can be
further exemplified with the optimal foraging theory, an ecological theory that
considers an organisms adaptation to its environment through an analysis of
energy cost and benefit (Dodson 1998).
In consideration of natural selection, we have the axiom:
Optimal Foraging Axiom 1: The organism with the highest rate of energy intake
will have the highest fitness.
Proposition (Optimal Foraging): An organism behaves to maximize energy gain.
Cost is the energy expended
to obtain food, often measured in units of time, and benefit the energy
acquired from a food source, usually measured in calories or by size. Dodson (1998) gives the example of a bee
foraging for nectar moving from flower to flower. The bee should theoretically remain at the same flower until
nectar levels are depleted enough that it would benefit the bee to move to
another flower with a fresh supply of nectar.
Thus, there should be an optimal time spent at each flower.
In actuality, the bee might
not spend the “optimal” amount of time spent at the flower as predicted by
measurement. What would be the
reason? There are a number of different
possibilities. Perhaps the measurement
of costs and benefits are simply incorrect.
Maybe the influence made by of other members of an organism’s species
need consideration, as they may add an element of competition or change the
distribution and/or abundance of a particular resource. There may be predation risks involved that
would make acquiring a resource more “expensive” than previously thought. The optimal foraging theory serves as a
starting point, which, after observation, needs modification. If the theory doesn’t work, the behavior
ecologist must revise the parameters, make new predictions, and retest. Thus, the new optimization model works for a
certain species or perhaps a subset of a species, rather than serving as a
universal model for all foraging organisms.
As Dodson (1998) points out, optimality is not what
is being tested. Rather, the goal
of the theory is to test hypotheses
on the adaptiveness of a certain behavior.
And according to Ruse (1988), any model, including population genetic
models using natural selection, is true until it is applied to reality, where
it becomes empirically true or false.
With evolutionary theory,
scientists are able to construct real, empirical processes, for example that of
sickle-cell anemia, where, despite the fatality of homozygousity, when an
indivdual has two sickle-cell alleles for a gene, the disease stays in the
population because heterozygotes, those individuals with one sickle-cell allele
and one normal allele, benefit with the accompanying malarial resistance. Thus, evolutionists work on a case-by-case basis.
Thus the question of
evolutionary theory as an axiomatic system depends on the degree of
specificity. Maybe evolutionary theory
can be axiomatized at a universal level, but this requires that the axioms
remain general. Mary Williams, who
attempted an axiomatization of evolutionary theory has been critiqued for being
too general (Ruse 1988). Is generality
good or bad? Some argue that it adds
strength to a system, others argue that nothing of specific interest could be
deduced with such generality. When
claims become very specific, they become applicable to only so many real-life
instances. Maybe the claim that
coniferous trees outnumber deciduous trees, as they are more adapted to the
environmental conditions is true in the Pacific Northwest, but it is a false
statement in South America.
Thus comes the argument that
evolutionists, rather than building a grand axiomatic system, often use
restricted models. It seems that what
evolutionists do is what they can do, and that is build a general axiomatic
system from which they can derive more specific information once applied to
nature. The route that evolution will
take in a certain case is dependent on the situation. Using past evidence that has as few differences from that which
you are trying to prove as possible will provide more predictive power. When claims become more specific in
evolutionary theory, as they must to explain many biological processes, they
lose their predictive power at the universal level. Is evolutionary theory then more descriptive than
prescriptive? As Wolters (1993) points
out, maybe evolutionary theory is a collection of “descriptive narratives” that
merely explain evolution. It is a
science that deals with events “after the fact” with “no scope for prediction”
(Ruse 1988). But, as the world is not
regular, it seems that the world is too complex, with too many variables, to
have much predictive power at a universal level. Surely many ecologists would argue that, given enough
specificity, there is predictive power in evolutionary theory.
It should make sense that
biology doesn’t always act according to overall general rules. It is itself a very encompassing science,
which uses many of the other “pure” physical sciences to help explain natural
processes. The elements of probability
and chance are a result of such complexity.
The reason they play such a big role is that there are so many factors
to deal with in nature. Rules change
over time and between environments.
Maybe evolutionary theory in biology is too wide ranging to be condensed
into a reasonably sized axiomatic system.
An evolutionary axiomatic system without modification of theory in
specific cases would be composed of too many if-then statements to count, and
therefore not practical. This does not
undermine the validity of evolutionary theory or biology as science. Any field of science cannot be 100%
predictive once placed in reality.
Darwin, Charles.
On the Origin of Species by Means of Natural Selection…London,
1859;
facsimile edition, Cambridge, Mass., 1964.
Dawkins, Richard.
The Selfish Gene. New
York, Oxford University Press, 1989.
Dennett, Daniel.
Darwin’s Dangerous Idea: Evolution
and the Meanings of Life.
London,
1995.
Dodson, Stanley L.
Ecology. New York, Oxford
University Press, 1998.
Greenberg, Marvin Jay. Euclidean and Non-Euclidean
Geometries: Development and
History. New York,
1997.
Malthus, Thomas Robert. An Essay on the Principle
of Population. London, 1798; rev.,
1803.
Ruse, Michael.
Philosophy of Biology Today. New York, 1988.