Space Exploration and Colonization

» carlzim - Chaos theory and successful innovations

Hi, folks:
Read this article and comment. It was publishedn on http://www.newrenaissance.com.

Thanks.
Carl Zimmerman

PREDICTING SUCCESSFUL INNOVATION FOR SPACE EXPLORATION AND
COLONIZATION
by Carl Zimmerman

"...The opportunity to create and build our own worlds from
scratch..."
Imagine this dialogue between a parent and teenager in the not too
distant future:
Parent: Why does your generation want to leave "mother" Earth
and colonize Mars? They will have to take great risks and pay
enormous costs in lives lost and material destruction if initial
attempts fail.
Teenager: Colonizing Mars gives us the opportunity to create and
build our own worlds from scratch. We don't have to waste our
lives dealing with problems inherited from previous generations
on Earth. We believe that justifies the risks and costs.
Parent: What problems?
Teenager: Ecological disasters and people conflicts--racial,
ethnic, sexual and personality--to name a few. In the Mars
colonies, we can't afford these problems. Survival depends on
innovation and cooperation based on contribution, not personal
style. For example, when an airlock problem is detected, there's
no time for debate. We'll listen to the autistic airlock expert
who can do nothing but fix airlocks and has a 100% history of
success.

We can't afford failure
This imaginary dialogue depicts the direction that the U.S. and many
other developed countries are taking today--space exploration and
colonization. Currently, the main goal is to develop new
technologies that will improve the quality of life on Earth, but as
the dialogue suggests, the colonists may "fall in love" with living
on other planets. This will especially happen if the cost of solving
major problems, such as ecological disasters, on Earth becomes
prohibitive. Models which predict the innovations that are likely to
succeed will improve the quality of life on Earth as well as for our
colonists on distant planets.
In some previous innovations, such the airplane, the consequences of
early failure were limited (e.g., a biplane crashing on the side of
a barn). For innovations needed for space exploration and
colonization, the costs of failure would be enormous, including, for
example, ecological disasters during testing on Earth and
destruction of entire colonies on other planets.

Predicting changing needs
and their impact on populations is
required to predict successful innovations
Ideally, in order to produce a successful innovation, we must
predict the essential needs of the space colonies, and identify what
does and does not exist today that will meet these needs. The "what
does not exist today" will tell us which innovations will have to be
developed. However, innovations based solely on "guesstimates" of
future needs and technologies run a high risk of costly failure if
these conflict with actual future needs. Innovations that reflect
only the present state-of-the-art without any assumptions of the
future will almost certainly fail. Innovations should be based on
the right balance between what we assume is needed tomorrow and what
we know will work today. How can this be accomplished? The following
will help:
If we depict a region and its components, including the population
and, for example, their possessions and architecture, after
classifying them an evolutionary model would emerge, suggesting a
"cast of characters" and behavior over time. To understand the
system and how its future may be affected by some event(s),
choice(s) and/or policy(ies), we would build a mathematical model of
the system as it exists at a particular time, and describe the
processes that will increase or decrease its different components.
We could apply the traditional approach of physics, that is,
identify the components and the interactions operating on these,
both to and from the outside world. From this, we can predict the
functioning of the system at that time based on the causal relations
between components, which we presume are present.
However, the predictions can be correct for as long as the taxonomy
(classifications) of the system remains unchanged (Aristotle fans,
please take note). The mechanical model of deterministic equations
for a given time cannot produce new types of objects and variables.
Its predictions will only be valid until some moment, unpredictable
within the model, when there is an adaptation to innovation, and new
behavior emerges. Consequently, we need models that not only predict
what future needs will be, but when they will change and what change
in population behavior will result.

Equilibrium does not exist in the real world
The basis of scientific understanding has traditionally been the
mechanical model. In addition to focusing on causal relations
between components at a particular time, it is assumed that this
system has run itself to equilibrium, so that the correspondence
between object and model is made through balance of variables at
equilibrium. In economics, for example, equilibrium is represented
by optimal utility for the actors, where consumers minimize cost of
goods and services, and producers maximize profits. This approach
assumes that all the actors know what they want and how to get it,
and are doing what they want given the choices available. However,
we know that equilibrium does not exist in the real world.
Today, system dynamics is available to replace this oversimplified
static approach, which is based on unrealistic assumptions, in
developing models for rational prediction of population behavior,
need and innovation. These are based on the following parameters:
Values of external factors, reflecting the "environment" of the
system, such as temperature, climate and soils, and expected
changes in these over time.
Values corresponding to population "performance" due to their
internal characteristics, such as technology, and knowledge
level and application, and expected changes in these over time.
Change comes from within the system
"If it's not in the heart, it's not in the head." (Salesmanship
proverb)
"The clouded mind sees nothing." (The Shadow, a fictional character)

Since system dynamics models required for predicting successful
future innovations are concerned with evolution, these must be model
systems in which adaptive and structural changes can occur. The
internal characteristics of the actors change endogenously, and new
variables and mechanisms of interaction can appear spontaneously
within the system itself, leading to a changed taxonomy.
In order to make the model work, it must be simplified. This can be
accomplished with two assumptions:
1. Events occur at an average rate.
2. All individuals of a given type are identical and of an average
type.

Chaos equations
are better predictors of successful future
innovations than bell curve equations
Errors introduced by assumption #1 (above) can be corrected by
probabilistic dynamics, which assumes that all individuals are
identical to an average type, but that events of different
probabilities occur. Consequently, probabilistic dynamics includes
sequences of events that correspond to runs of good or bad "luck"
and their probabilities.
Systems with nonlinear interactions between individuals eliminate
the concept of a simple, constant trajectory. Evolution of the
system will be described by a probability distribution that
gradually changes in shape from a single modal "bell curve" (sharply
peaked and centered on a mean) to spreading and splitting into a
multi-modal distribution with peaks that correspond to different
attractors of the dynamics (attractor=position where the dynamic
system converges). These attractors could be point or cyclic, but
most likely, based on previous experience, will be chaotic.
Since unpredictable runs of good or bad "luck" will occur, a precise
trajectory of the system does not exist for predicting future
behavior. Also, these deviations from the average rate of events
means that a system can "tunnel" through apparently impassable
potential barriers, and can switch between attractor basins and
explore the global space of the dynamic system in a manner that the
system cannot predict.

Adaptations For Space Colonization
Since the innovations for future space exploration and colonization
will be dynamic systems, the relationships of the system variables
will be nonlinear. Consequently, we expect chaos equations to
provide very useful descriptions of relationships for
mission-critical biological, ecological and economic systems.
Already electronics hardware such as solid state lasers,
oscilloscopes and analog computers make extensive use of chaos
equations. We've only scratched the surface of their potential.
Ed: In future issues, Carl Zimmerman will expand on the promising
applications of chaos theory and its advantages for future pioneers.

About The Author
Carl Zimmerman is a techno-marketing writer in the U.S. for a
major global manufacturer of animal nutrition and health
products. He has a B.S. degree in chemistry and an M.B.A. in
marketing.

-- posted by carlzim

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» scottishgirl - Carl,

A link to the website might have been sufficient, along with a start of a discussion about the topic. If you want to put in an application to become a Contributing Editor on this topic for Suite101, you can fill out an application online here.

This isn't really a forum for articles, but for discussions on things. Did you have anything you wanted to say that might be more, I don't know, gabby? :-)

-- posted by scottishgirl

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