Getting Practical

So why does any of this, any of any knowledge, matter? Why pay
attention to the structure of reality, to rules of arrangement, to
domain independent meta-pattern and meta-process, to universal
grammars, to stacked grammatical hierarchies, to causal ontologies
and influence cones, to complexity metrics?

Greater knowledge of the dynamics of the system you are a member of
yields efficiencies in processing and access of resources. That is
the game. That is evolution. That, like it or not is the game you
were built to play. And, in this game, the board, the players, and
the rules are evolving as well. There is no standing still. You
play or you get played. You eat or you get eaten.

In this game called evolution, there is profound advantage to being
at the apex of control, the apex of complexity. Know more and you
will exploit more completely and cleanly and your exploits will shape
the future of exploitation, of evolution itself.

We have a tendency to think nature and the cosmos as static. We
romanticize concepts like natural cycles and balance. These are side
effects of our short perspectives, our limited vistas in time and
distance. Our daily experience is restricted to a few tens of miles
and a memory that breaks down at the scale of a year or two. As a
result we think we see yearly weather repeat itself, we think we see
landscapes and vistas as fixed and unchanging. Once the airport
construction is complete, we are at odds to remember what was there
before. We read and try to absorb concepts of much larger cycles,
warm and wet to cold and dry epochs that stretch out over the scope
of tens of thousands of years. Continents that split and drift the
circumference of the globe to smash into each other at the speed our
fingernails grow (a meter a century!). We are experientially retarded
to the very idea of events that are a hundred thousand or a million
times our own life span.

Yet the patterns that matter, that connect our little lives to the
history and breadth of the universe and to the equally distant, but
miniature world of the particles we are made of... these patterns
must be divined and abstracted and forced into simplified and cleaned
up forms acceptable to our very scale-challenged little brains.

We have a tendency to look to the past as a slightly more primitive
form of the present. How many of us can truly grasp the 4.5 billion
years our little Earth has been in existence? Imagine for instance
that our moon was closer when it first formed and even 1.5 billion
years into our planet's history, daily tides were 1000 feet tall.
Oh, and by the way, our moon came at a heavy cost. Just 35 million
years after the earth had reached something like its current modern
size, it smashed into another planet almost as large. The resulting
reverberation of melted rock in space left much material orbiting as
mini moons that slowly smashed into each other to form a single
mass. Remember here that the earth is not now and certainly wasn't
then a solid chunk of rock. The hard cool stuff we experience on the
surface of our world is ridiculously thin, comprising just .7% of its
mass. Animals, the multi-celled kinds of life we think of when we
think of life, you know, with heads and limbs of some sort, came into
existence just three quarters of a billion years ago. Plants first
came out of the water 450 million years ago. That means that for
more than eight ninths of the earths history, there were no plants of
any kind anywhere except in the oceans. Flowering plants didn't
appear until just 130 million hears ago... just one three hundred
fiftieth the age of Earth! The first primitive primates didn't show
up until about 60 million years ago, the great apes appeared just 15
million years ago. Modern humans have existed less than 250 thousand
years. Written language less than 5 thousand years ago.

Then there is the considerably larger scale of the evolution of the
Universe itself, which did its own thing for about 9 billion years
before bothering to build the Earth. Our galaxy is a collection of
second generation stars and stars don't exactly have short lives.
First generation stars were made of the only elements left over from
the big bang, the simplest ones; mostly hydrogen, helium, and
lithium. Anything you could build a rocky planet from simply didn't
exit. The stuff of stuff, rocks, water, air; none of these things
could exist at all until the nuclear fusion caldron that is an
imploding first generation star.

Only the debris (as atomic dust) from the stupendous death of a first
generation star can create the kind of hard stuff (the heavy matter
populating the periodic chart) that will swirl around its own
distributed gravity well and end up accumulating into a new sun and
its attendant planets, rings, dust clouds, astroid belts,
planetesimals, etc.

Nothing stands still. Nothing is truly cyclical. Even the wildly
energetic forces that hold electrons in orbit clouds around an atom's
nucleus loose a little push with each moment that passes. Every
action has its cost. Every action irreversibly changes the
parameters of the game. An orbit is in reality a spiral. A sun is
burning itself out. A fox today is different from what at fox was 30
generations ago. An astroid is a chunk set free from the collision
of two planets large enough to melt heavy elements in a chemical
furness fired by the friction and pressure of its own collective
gravity. A storm this year is in fact different (if only slightly)
from a storm any previous year. Even the word "evolution" means
something different today then it did just months before. Returning
to any previous state is an illusion. Time and its attendant
dynamics force irreversibility on all systems.

If we are to know process, if we are to understand the most basic
parameters of the universe we live in, were produced by, and now play
an active and collective roll as creator, then we must switch our
impetus of understanding from things, places, and events to process,
change, and evolution.

The stuff, the current incarnation of process, is ethereal. Only the
process of change, and the rules of change, remain eternal and
unchanging. But don't choose to be attracted to change because it is
somehow qualitatively more interesting than stasis... it isn't.
Interest your self in change only because reality is set up in such a
way as to make it causally superior to a lack of change. The rules
of change create the the stuff and the dynamics of stuff and any
current state, never the other way around. The laws of change are
more causal than the dynamics of stuff and of domains of stuff. The
laws and parameters that govern change give rise to the subservient
laws that describe stuff and the dynamics of stuff. Not the other
way around.

As culture (the collective collection of what we know and how to
apply it) becomes a more and more accurate and complete abstraction
to process, humans exert greater and greater control over their
environment. Much of this exploitive power was achieved before we
had any conception of the laws of change.

-- more to come --

Why Your Point Looks Like My Point

I've spent years thinking about ways to represent (diagram, explain,
summarize, illustrate, simplify) systems that expose deep and real
truths or salient aspects that would otherwise be hidden behind the
full cacophony of the whole system in situ. That is, after all, what
abstraction is all about, a filtering away of what doesn't matter and
towards what does. Sounds simple enough... but filtering and
representation as simplified abstraction that magnifies deep patterns
and vacuums away the rest is to my reckoning exactly why evolution is
such a slow, step-wise, intractable, and energy hungry process. Of
course it is instructive to remember that abstractions are no less
real than the systems they represent. In fact, one can make an
argument that evolution produces abstractions, that the current state
of any system is a layering of abstractions that shape the morphology
of a system such that it can exploit more effectively the resources
in its environment. And while the focus of this discussion is the
kind of mental or notational mapping done by humans, this is only one
way that nature has found to represent and thus simplify the access
and processing of external resources.

In particular I am focusing in on diagrammatic representations of
systems as network maps that represent the causal relationship
between the parts or sub-systems that make up a system. I call these
ontologies, "hierarchies of influence". Influence hierarchy maps
naturally take the shape of cones and I am interested in qualitative
differences between the point-y and funnel-y ends of these cones. In
particular, I am curious about the apparent commonalities of these
cones at their lowest or most causal points.

So what exactly IS an "influence cone"? The concept is based on the
idea that all elements in a system proportionally effect or are
effected by all other elements in that system, and that these
relationships can be represented by a network diagram or ontology.
Once all elements in the resulting influence hierarchy map are
optimally arranged to minimize link length, a spacial arrangement
will appear with cause/effect range across the dominant axes (things
that cause on one end, things effected on the other).

Think about it, given any two interacting elements, one is always
going to exert more control over the other, is going to cause more
than it is effected by the other. If you take all of the elements of
a system, all of the parts and subsystems that together result in the
shape and behavior of a system, if you take all of these elements and
arrange them so that the ones that cause more find their way to one
end and the ones that are more effected are at the other end of the
spectrum then an ontology as network of influence will result and
this network will be shaped like a cone. Because it is easier to be
effected than it is to cause, there will always be far more elements
on the effect (or wide) end of the cone, and the other end will taper
to a point where sits the one or two elements that end up effecting
everything above them and are not themselves controlled by any other
elements. These most causal elements at the base of the influence
cone are one-way linked to other elements... instructions travel out
from them but rarely travel back the other way. The same (in reverse)
is of course true of the effect end of the cone, these elements are
more likely to be one way linked to other elements that effect them.

Depending on which relationship parameter is being scrutinized, a
system can of course be represented simultaneously by many influence
cone abstractions. Further complicating reality, the definition of a
system is arbitrary, and the same element or subsystem can appear in
an almost infinite number of systems each with their own almost
limitless set of influence cone mappings.

One can imagine building influence cones of other influence cones or
more provocatively, super-imposing multiple influence cones, building
an n-dimentional super-cone of all possible influence cones. In such
a super-cone, an element shared by multiple cones would exist not as
a point but as a probability cloud. Never the less, one can imagine
that the the causal end of the resulting super-influence cone would
share, in some rough probabilistic way, the causal ends of many sub
cones.

This most likely explains the coincidences and serendipitously shared
concepts scientists and philosophers frequently expose when comparing
multiple domains and disciplines at base or deeply causal ends.

Anyone who has kept abreast of progress made in the sciences over the
past 100 years will have been curiously struck by strange conceptual
parallels that show up across such seemingly separate fields of study
as information science, thermodynamics, linguistics, bioinformatics,
genetics, genomics, evolution, AI, and many of the attempts to build
a Grand Unified Theory (GUT) to explain the universe from first
principals. Another way of explaining away the apparent parallels
across the causal base of all domains is to say they are a byproduct
of the ignorance that is a natural byproduct of exploring at the edge
of the known. It is by definition grey and blurry out at the fringe
of the known. So the question we have to ask ourselves constantly is
"just what is it that seems familiar; real patterns or the fact that
all patterns blurred sufficiently will appear equivalent?" Truth or
just another shade of grey?

Yet, I don't think we are being fooled by our own senses, because I
see patterns come into sharper and sharper focus as our knowledge
increases. But, there is a third and more problematic reading. The
third reading is built on the assumption that we are making progress
teasing accurate abstractions from the order inherent in nature.
Patterns seen at base of all domains of study are assumed to be
reflect actual similarities... but these similarities are assumed to
be attributes of mapping, of limitations built into our abstraction
process and say nothing about the actual shape and behavior of the
systems they represent. This is the post-modern position. Hard
postmodernists refuse to acknowledge the possibility that any real
pattern exists beyond our mapping. Medium postmodernists say reality
might have pattern but because we can not see it without abstraction
it doesn't matter either way. Soft postmodernists think the reality/
mapping activity will introduce signal/noise confusions that are
unavoidable but that working knowledge grows as our map gets better
and better at understanding and mitigating this problem.

Personally, I am loath to the obstinate arrogance and human-
centricity that practically oozes from the self inflicted wound that
is postmodernism. Modern humans have only been here to share this
corner of the universe for fourteen ten thousandths of the history of
this universe. If reality is dependent on our experience of reality
(this is the honest-to-god position of the hardest postmodernists),
then how did the universe go about its business long enough to create
us in the first place? However, to the extent that abstraction
methods do end up clouding and obscuring our view of reality, we must
continue to pledge vigilance against the demon that works tirelessly
to confuse understanding. The very fact that this category of noise
generation has been exposed is proof that the hard relativism of the
hardest postmodernists is wrong. The fact that we continue to learn
to recognize (and mitigate the destructive effect of) more and more
subtle sources of measurement, observation, and mapping noise, means
that our maps will become more and more accurate. Post-modernist
cautions have led to protections that have made science that much
more accurate and authoritative.

Mapping methods can indeed superimpose their own grammatical
structures over any raw subject being mapped. Plus, we have a
tendency to re-use familiar mapping techniques (description
languages). If these english words have worked to communicate the
shape and behavior of a mouse, why not use them to describe the solar
system, or the English language itself? Again we come to a
crossroads of mapping cautions, again we are visited by the taunting
ghosts of Penrose, Godel and Turing. Mapping, abstraction, useful
understanding is stronger for it.

Limits vs. Hard Limits

I found the following pages (links below) about physical and logical
limits. The author posits that true limits are frequently and
practically the result of knowledge systems themselves. My take on
his argument is that even where true limits exist (Godel, Penrose,
Turing) the limits in our own notational, logical and processing
systems prevent us from ever experiencing the true innate limits in a
system.

My guess is that Godel's incompleteness limit and Turing's halting
problem, and even Penrose's arguments about self same limits imposed
by the false mapping of one computing or mapping system onto a domain
with its own (incompatible) processing system. Again, these logic
and processing system miss-mappings may present false limits that are
fundamentally different from any true limits that may exist, and
importantly, one might mistake the false limits for the real one(s).

Interestingly, Turing's process halting proofs prove that all of his other work on computing system equivalence may never be conclusively
applied to a given process (as it is impossible to say whether or not
a given process is computable (will not halt) and his equivalence law
depends on a process being computable.

The work of most theorists depends on the notion that the universe is
computable, is Turing complete, is not a member of the group of
programs that will halt. Even more problematic is the work of theorists who attempt to build ad hoc simulations of the most causal layers of the universe, of its origin, in which case both the simulation and what it simulates must both be Turing complete and not a member of the set of halting programs.

My mind is whirling around all of these issues. Plus, I am noodleing around the notion that the lowest levels of hierarchies of influence
cones (more later) necessarily share commonalities (even become
equivalent) at their lowest or most causal point. If that is indeed
the case, then there is a reason that I am seeing such parallels
between GUTs, information science, thermodynamics, linguistics,
bioinformatics, genetics, genomics, evolution, and AI. The other
less appealing possibility is that these apparent similarities
between the base of all domains is a byproduct of the ignorance that
is a natural byproduct of exploring an edge of what is known.

http://www.fortunecity.com/emachines/e11/86/crashbar.html
http://www.fortunecity.com/emachines/e11/86/loglimit.html

Note: The author of these linked pages is JOHN L. CASTI a professor
at the Technical University of Vienna and at the Santa Fe Institute.

Productivity at Base

I have an economic question or two. Why is monetary policy
positioned at the apex of control in our nation's economic
hierarchy? Why is the Fed Board Chairman the go-to lifeguard and man-
on-the-mountain-guru for our entire economy? Failing a lowering of
the prime lending rate or the fed rate, we don't seem to know how to
intervene or plan the economy such that moments of panic necessitate
these quick fixes (that only harm the economy in the long run). And
this makes me confused about everything I have learned and everything
that makes sense about economic theory. In academic circles, there
seems to be a general acceptance of what might be called the standard
model of economic science. From what I have read, serious scientists
of economies almost always agree that productivity alone sits at base
of all influencers at play in any economic system. Ultimately, this
means that other factors weigh in more on the side of effect and that
productivity is THE factor that more often than not is more causal
than any other factor effecting economies. Modelers of economic
systems, like modelers of global climate, implement countless
mathematic dynamics seeking mathematic descriptions that can robustly
mirror and follow the arch of actual economies under actual natural
pressures over time. In these models, the ones that can achieve some
success aping real economies at global scales... productivity rises
to the top of every influence hierarchy. So what is productivity?
How is it different from other metrics of economic influence like
spending, commodity, resource, stock, currency, and geopolitical
trade markets, saving rates, inflation, jobless rate, secondary
education rate, incarceration rate, capital investment rates, basic
research spending, infrastructure amortization, how indeed is it
different from large scale economic measures like GDP itself?

The concept backing the term productivity is a more complex than
other common economic measures. Like evolution it is obvious that it
is central to and at base of the inverted influence pyramid... but
like evolution, it seems also to be a moving target... like the
shadow of a person walking east in the evening, it is right there, it
has a finite length, yet one never catches or completely possesses
it. Wow that is a metaphor out of control. Actually, a moving
target is just what you would expect in a dynamic system that feeds
on it's tail, that is different tomorrow because of what happens
today. And, like evolution, productivity defines the propensity of
today's systems to maximize the effectiveness of tomorrow's systems.

The problem with Fed Rate finagling and other Monetary Policy
doodling is that, like lifeguards flinging life rings, is to public
and to immediate, to much of a band aid, to much after the fact, and
we soon forget that the very use of such stop-gap measures is usually
a good indication of deeper ills, ills that can not be truly fixed
with heroics (with all the screaming and running around, and with all
of the cowboy heroics and wasn't that close brow wiping and back
slapping afterwards, who is going to remember the importance of
swimming lessons and civic behavior, and safe pool design?). Here
come the bank chairman calling out for a quick fed fix (read subsidy,
read absolute disincentive to act responsibly or to care about the
health of the economy) and the here comes the Fed Chairman on his
white horse again to provide a temporary high level fix to what is by
definition always the result of deep low-level wows. And here we
are, the public, by practical necessity (our busy lives) ignorant of
the subtile complexities that make up the grand causal stack that is
the economy, anxiously anticipating a quick fix, so that we can go
back to our blissfully simple understanding of the economic world
around us.

I frequently use the term hierarchy of influence to remind myself and
others that every complex system is an assembly of parts arranged by
hard natural law into an influence tree where some parts have greater
influence on change than others do. On the bottom of this tree
influence tree are the things that cause other things, as you move up
the tree you find things that are more caused by or are the effect of
other more influential things below them. Cause and effect are very
very different. This is the important concept to get. Consider a
simple system, a steel bolt laying on a concrete floor with a magnate
laying on a bench a few feet away. The prime influencer in this
system is gravity. If you move that magnate closer to that bolt, the
influence hierarchy will at some point flip when the attractive force
from the magnate is stronger than the gravitational force between the
bolt and the earth. At all times one must remember, both forces are
at play, it is just their relative influence that changes. The
physical and behavioral state of all systems at any given moment are
simply the sum of all influences at play within them. And these
influences are not equal. If your goal was to move that bolt, it
would be ridiculous to spend much energy worrying about the
orientation of that magnate on the bench. Yes, spinning the magnate
does have some (miniscule) measurable effect on the system... on the
bolt, but there are other potential influencing factors that will
have far greater effect on the system (moving that magnate within a
few inches of the bolt for instance). Same goes for the economy of
course. If you were given the task of defining the indispensable
factors that would absolutely have to be present in a healthy,
growing, regionally and globally competitive economy, prime rates and
cash fluidity would probably not enter the picture until many more
profound factors were taken care of (resource availability, trained
and knowledgeable labor base, save and stable living conditions that
promote individual well being, transportation and communication
infrastructure, physical and virtual markets (where to buy and sell
things), ownership protection, ready availability of credit for
capital expenses, etc. It is when the existence, availability or
balance of these systems fail, that stop gap measures like currency
and lending rate control become necessary.

The Biggest Why

Here is my attempt to explain my self and my work.

My goal is to contribute ideas, knowledge, tools and infrastructure
that will help humans understand and exploit the most pervasive and
powerful structures, processes and agents of change... towards
accelerative increases in productivity. I see this as the only
process that matters. To the extent that we stay attentive to the
process that is change, productivity increases apace. But what is
this process and how can I claim to know that it is THE process?

I began this exploration as a 9 year old one afternoon while walking
home from school. That was the day I decided to spend my life
looking for a truth or set of truths that sat at base beneath all
other truths, that informed and gave shape to all processes, a truth
that was independent of domain, that was true and formed the shape of
all things and all processes. I also made a promise to myself that I
would reject any of the theories I came upon or created if even one
small measurement conflicted or even worse, if my theory called into
question any other empirically verified theory. In short, I would
accept ideas only if they were in complete agreement with everything
else that was known to be true (verified by measurement). Over the
next 10 years I worked several theoretical epochs to this abortion
point where my stringent test of data agreement was violated.
Because the truth I was looking for had to be independent of domain,
because it had to be as true and as primary to particle physics as it
was to quantum electrodynamics as it was to the Krebs Cycle it was to
atmospheric dynamics as it was to galaxy and super galaxy evolution,
as it was to market fluctuation, and cultural evolution. etc., I was
forced to look to meta patterns and meta dynamics. Thanks to the
good people who independently discovered thermodynamics and
information theory, two sets of identical math that show the
absolute equivalence of structure and energy, two theoretical
frameworks that describe the parameters and limits that govern change
in any system, I had a solid scaffolding or armature with which to
give definitive structure to the more bio-centric theories of change
outlined by Darwin. I began to from a mash-up theory of change in
any domain, rooted in evolutionary theory and informed by
thermodynamics/information theory.

We are familiar with these ideas in the pedestrian; in business and
economics we collectively call the result, productivity. In
evolution this is the elusive arrow of time, this is that wily
fitness that determines whether a bit of DNA will be more or less
represented in future branching of the tree of life, this is why any
tomorrow is qualitatively different than and dependent on any today.
At base my work is founded on the theory that complexity increases
over time in small regions of larger systems (or THE system) simply
because complexity gets energy and structure to degrade towards heat
and random distribution faster than without complexity. This
degradation of order over time is domain independent and drives all
change. Most of the random accumulations that result are simpler
than the order from which they precipitate, most of these byproducts
of action are unstable and short lived. Once in a great great while,
a novel structure falls out with the other detris of action, and even
rarer, this novel structure is both stable and generative... causes
its own out-fall of debris. This is the process of evolution. This
is the reason it happens. If degradation of order is the most
universal of processes, then it is the base pressure behind change,
it is the why of evolution. With this knowledge we can know
important things about all process. That no action happens except
the process that takes the least energy. That competition between
structures is competition to degrade energy and structure faster and
more completely. That structures that are more fit by these
standards will inform the structure of the future of complexity more
than structures and processes that are less fit. That fitness is in
fact a measure of a system's ability to create structures of even
greater fitness over time. That this metric is a property or the
property of value to the universe (or any universe). That this
metric represents a moving target, an n-hard problem, a solution
built of terms from its previous state, is by its very nature not
deterministic. The end-state is knowable; heat-death. The process
is knowable; optimization of structures that maximize the production
of sustained entropy. But the exact most optimal solution at any
given time is unknowable and additive. Understanding this process
should yield growing efficiencies can never result in the perfect
solution.

As I said before, I am interested in domain independent truths. One
of the conceptual tools I use is what I call hierarchies of
influence. A hierarchy of influence is a cline stretched from pure
cause to pure effect. It assumes that some parts of a system are
more cause and some are more effect. I like to think of these
hierarchies of influence as inverted cones where one will find the
most fundamental influencers near the bottom point and the derivative
cause agents above them. In fact, real systems are more than
probably not so suited to simple diagrammatical organization... but
it works for me to think this way. I'm sure many people will say
that their work will eventually sit as THE primary causal agent at
the base of the most universal influence hierarchy cone. I almost
agree. I agree that some theory will eventually explain, even
ferment, all theories above it. A GUT theory! For this universe
anyway. And this is why I posit a tangential influence cone. One
that is abstracted to the point that it has to be true no matter what
self consistent set of physics your universe is derived from.