S-curves are typical of biological growth: replication of a system of relatively fixed complexity (such as an
organism of a particular species), operating in a competitive niche and struggling for finite local resources. This often
occurs, for example, when a species happens upon a new hospitable environment. Its numbers
will grow exponentially
for a while before leveling off. The overall exponential growth of an evolutionary process (whether molecular,
biological, cultural, or technological) supersedes the limits to growth seen in any particular paradigm (a specific S-
curve) as a result of the increasing power and efficiency developed in each successive paradigm. The exponential
growth of an evolutionary process, therefore, spans multiple S-curves. The most important contemporary example of
this phenomenon is the five paradigms of computation discussed below. The entire progression of evolution seen in
the charts on the acceleration of paradigm shift in the previous chapter represents successive S-curves.
Each key event,
such as writing or printing, represents a new paradigm and a new S-curve.
The evolutionary theory of punctuated equilibrium (PE) describes evolution as progressing through periods of
rapid change followed by periods of relative stasis.
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Indeed, the key events on the epochal-event graphs do correspond
to renewed periods of exponential increase in order (and, generally, of complexity), followed by slower growth as each
paradigm approaches its asymptote (limit of capability). So PE does provide a better evolutionary model than a model
that predicts only smooth progression through paradigm shifts.
But the key events in punctuated equilibrium, while giving rise
to more rapid change, don't represent
instantaneous jumps. For example, the advent of DNA allowed a surge (but not an immediate jump) of evolutionary
improvement in organism design and resulting increases in complexity. In recent technological history, the invention
of the computer initiated another surge, still ongoing, in the complexity of information that
the human-machine
civilization is capable of handling. This latter surge will not reach an asymptote until we saturate the matter and energy
in our region of the universe with computation, based on physical limits we'll discuss in the section "... on the
Intelligent Destiny of the Cosmos" in chapter 6.
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During this third or maturing phase in the life cycle of a paradigm, pressure begins to build for the next paradigm
shift. In the case of technology, research dollars are invested to create the next paradigm. We can see this in the
extensive research being conducted today toward three-dimensional
molecular computing, despite the fact that we still
have at least a decade left for the paradigm of shrinking transistors on a flat integrated circuit using photolithography.
Generally, by the time a paradigm approaches its asymptote in price-performance, the next technical paradigm is
already working in niche applications. For example, in the 1950s engineers were shrinking vacuum tubes to provide
greater price-performance
for computers, until the process became no longer feasible. At this point, around 1960,
transistors had already achieved a strong niche market in portable radios and were subsequently used to replace
vacuum tubes in computers.
The resources underlying the exponential growth of an evolutionary process are relatively unbounded. One such
resource is the (ever-growing) order of the evolutionary process itself (since, as I pointed out, the products of an
evolutionary process continue to grow in order). Each stage of evolution provides more powerful tools for the next.
For example, in biological evolution, the advent of DNA enabled more powerful and
faster evolutionary
"experiments." Or to take a more recent example, the advent of computer-assisted design tools allows rapid
development of the next generation of computers.
The other required resource for continued exponential growth of order is the "chaos" of the environment in which
the evolutionary process takes place and which provides the options for further diversity. The chaos provides the
variability to permit an evolutionary process to discover more powerful and efficient solutions. In biological evolution,
one source of diversity is the mixing and matching of gene combinations through sexual reproduction. Sexual
reproduction itself was an evolutionary innovation that accelerated the entire process of
biological adaptation and
provided for greater diversity of genetic combinations than nonsexual reproduction. Other sources of diversity are
mutations and ever-changing environmental conditions. In technological evolution, human ingenuity combined with
variable market conditions keeps the process of innovation going.
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