While Life computers are generally not feasible, Life has many uses
that might not be readily apparent. Its main use is to model complicated
systems of interaction. For example, weather forecasts depend on many
different principles each affecting other factors. Temperature, winds,
and humidity are not all uniform across the sky and each individual part
cannot be added into a formula to definitively come up with a weather
forecast. The same principle applies to parts of an ecosystem: all of
the
components depend on all of the other components and cannot
accurately be modeled separately.
In addition to modeling ecosystems,
the Game of Life can also be used
to model a living species over time. Assuming an infinite amount of
combinations in Life, it would make intuitive sense that at least a few
would be self replicating. Von Neumann showed in his introduction of
cellular automata that this would be possible because a series of Life
combinations can be set up to produce any other combination with the
correct instructions. Thus, it could be programmed to produce itself,
and
to continue this process as long as the instructions (the Game of Life
version of DNA) are passed on to the creation. Over time, those
combinations would produce more of the same combinations and
inevitably, "mutations" would result. For instance, a creature
could run
into a block or another still life that would alter its shape from its
original
form. Most of these mutations would result in the creature no longer
being self-replicating and eventually dying off, but some should over
time be beneficial, enabling creatures to reproduce better. Thus these
beneficial mutations would dominate eventually until another beneficial
mutation came along. By watching the Life structures evolve, one could
witness the possibilities present in evolution and gain an idea of the
beginning of life on Earth. Self-reproduction and successive
improvement through mutation have wide-spread implications in
Artificial Intelligence.
The possibilities for modeling
systems using Life have only begun to
be explored, leaving many directions in which the same ideas can be
focused to produce further improvements. The simple and powerful
model, similar in many ways to biological life itself, will have lasting
implications to our current understanding of many currently unreachable
phenomena.
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