Self And The Phenomenon Of Life: A Biologist Examines Life From Molecules To Humanity

Self and Free Will 241

“9x6” b2726 Self and the Phenomenon of Life: A Biologist Examines Life from Molecules to Humanity

motion are prime examples of determinism,^5 giving us a sense of clock-
work precision, leading to the proclamation by Laplace (1749–1827)
that, if he could know the positions and movements of all the heavenly
bodies at one moment, he would be able to predict the outcome of the
universe well into the future ad infinitum. Of course, Laplace’s assertion
is unverifiable because no one can ever have complete knowledge of the
universe at any given moment. This boils down to the issue of complex-
ity. Although isolated events are easy to predict, a complex network of
causal chains frequently thwarts our forecast. And the world is an enor-
mous, entangled causal network.
Let me start with a simple example. Suppose I stand a sharp pen-
cil on end. Can I predict to which direction it will fall? In principle, a
perfectly balanced pencil should never fall. But in real life it will. The
standing pencil is under the influence of innumerable forces other than
gravity: inclination and friction of the table surface, air flow and tem-
perature fluctuations in the room, and building vibration due to wind
speed or rumble of a passing train. Other distant factors may include
an earthquake in a remote corner of the continent, plate tectonic move-
ment below the ground, wobbling of the rotating Earth, the pull of the
moon, or explosion of a distant star. Some factors are so weak that they
are mere “noises,” but they will show up when the major factors cancel
out. So the pencil will fall, no matter how well I balance it. The more
perfectly I try to control the variables, the more unpredictable will be
the direction of the fall.
Take the case of weather forecasting. In the model called chaos
theory (deterministic chaos), a nonlinear dynamic system can gener-
ate random, non-periodic, widely divergent outcomes that are hard to
predict, depending on minute differences in the initial conditions. First
hinted at by the mathematician Henri Poincare, it was discovered in
the twentieth century by Edward Lorenz when he tried to solve three
simple equations for the motion of the atmosphere.^6 The weather pre-
dicted by a computer varied widely when initial values differed by less
than 0.1%. Thus, a butterfly flapping its wings in South America can