PREDICTIONS – 10 Years Later - Santa Fe Institute

11. FORECASTING DESTINY
related to energy theoretically through the concept of negative entropy,
the amount of order. As the order decreases in a system, the amount of
meaningful information in it diminishes, and so does its energy. The
implication in simple terms is that knowledge is paid for with energy.
The smallest bit of information is a yes/no knowledge, the on/off state
of a transistor. In units of energy (ergs), it has an energy content equal to
2x10 -14 .
Efficiency in thermodynamic terms is defined as the ratio of output
(useful energy) divided by the fuel (energy input). The efficiency of a
computer’s CPU can then be defined as the ratio of the energy equivalent
of one bit of information divided by the amount of electricity used
to change the state of one transistor. This ratio turns out to be in the
range of 10 -11 to 10 -10 . It means that, relatively speaking, computers
generate enormous amounts of heat for their calculations. This can become
a formidable obstacle for applications involving enormous
numbers of calculations.
Miniaturization makes computers more efficient because the higher
the density of components on an integrated circuit, the lower the consumption
of electricity. Economists would justify miniaturization
through the money it saves and argue that the price of energy should
have an impact on the evolution of efficiency. One can prove them
wrong by demonstrating that the evolution of efficiency has deeper
roots and long-term cultural links, and is not affected by the availability
of energy or changes in its price.
The evolution of efficiency over time documented with data points
for three selected technologies yielded three distinct straight-line segments
(plotted in the non-linear scale introduced in Chapter Six). One
was for prime movers (engines), with its beginning around 1700. Another
one, the technology for making light, started in the days of the
candle. The third one was the technology for producing ammonia in the
twentieth century. Today all three are around the 50 percent level (Appendix
C, Figure 11.2).
Becoming more efficient is a learning process; consequently the
evolution of efficiency is expected to follow an S-shaped naturalgrowth
curve. The fact that the data points fell on straight lines shows
the natural-growth character of the processes. The data adhered to the
natural-growth paths even though energy prices surged manyfold on at
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