The Life And Times Of Your Average Paradigm

Systems are in constant state of flux, they change all of the time, over time, and even when they don't or can't, the environment around them changes in response to their behavior or simple presence.

Systems evolve. The super-systems in which they live, evolve. It's what happens, it is the only thing that can happen. Stuff constantly adjusts its behavior in response to the stuff around it. And things can not help but mess with the things near them. Change is inevitable. But more than that, change has pattern that can be teased out, measured and described.

These patterns are generalizable and can be found in all systems regardless of domain. All systems evolve. All evolution is similar. What Darwin described in biology, once generalized, can just as accurately describe the interaction of gases or the layered persistent structure of ocean currents, or the way I came to these thoughts and decided to write them down.

An interesting aspect of systems is the way they are made up of layers of subsystems each bound by unique structural and behavioral rules, and all of this can exist simultaneously across many dimensions. These 'layered grammars' are perhaps easiest to see in language, where symbols are assembled in ever more complex aggregates (phonemes, words, phrases, sentences, paragraphs, themes, sections, volumes, collections, etc.), each governed by its own rules of construction.  Of course an utterance can be parsed by the layered rules of symbolic grammar (as above) or any other set of layered grammars… take for instance it's semantics or meaning.

But what interests me today is the life span of a system. Though it is problematic to do so, it is often useful to define, at least loosely, the beginning, middle, and end of a system's life span, the arch of its development through time. Individual humans have life spans of course, and from a more distant vantage, so too does a culture, and though the arch of of these classifications hasn't run its course, the human species. From ever wider vantages, one can talk of the stacked life span of hominids, great apes, primates, mammals, chordates, multi-celled animals, eukaryotes, and biota itself.

What interests me here are the patterns can be teased from any life span? More to the point, the patterns that are universal across all life spans. What, for example, is there that can be accurately, and predictively said, of the difference between the first half and the second half of any life span? What is it about the beginning of an individual human's life that is similar to the beginning of the life span of the human species or the beginning of the life span of life itself?

A reasonably robust set of these life span meta-patterns might work well as a way to better define the boundaries that give meaning to the most general concept; "system" ("category", or "thing").

But what I find most valuable about this strategy, is the possibility of predicting the relative age of a system without ever having witnessed the full arch of a life span, as example. Is the system of focus in its infancy, is it a teenager, or is it middle aged, old, or nearly dead? Are there reliable parameters that can be mapped over a system to help us determine such things? I am convinced there are. My confidence in this guess stems from the dramatic symmetries that have been exposed over the past century and a half in the fields of information theory, thermodynamics, classical physics, and quantum dynamics, linguistics, and logic. What this work has exposed is equivalence transforms that show causal connections between energy, mass, time and distance, and importantly, information. This overarching symmetry hints at symmetries in systems themselves and in stacks of systems, and the way systems change through time.

It is this knowledge these profound symmetries, uniting such apparently separate systems, that best describes the most important contributions of the last century of scientific exploration. Wielding this knowledge, we can use the same language and logical tools to examine any system, be it physical, behavioral, or descriptive, or cognitive.

The slippery and ghostly similarities we have noticed across domains, the ones we previously chocked up to metaphor, have been shown in fact to be causal and real (and we have the math to prove it!).

It is frustrating, that the topics I am most interested in, require the assembly of so much preliminary conceptual scaffolding. All these words, and I haven't even gotten to my main point. Here goes.

I talk often of what I call "productivity paradigms". They are ethereal and mercurial economic entities defined by some factor that gives rise to previously unachievable levels of the value of an average hour of labor.

As systems, productivity paradigms should avail themselves to the kinds of 'life span' parsing we would apply to any system. So, we can ask things like: can we determine the relative age of a given productivity paradigm?

And, is it possible to can we know this from the rising or falling rate of growth resulting from that paradigm?

Are these questions, addressed as I have, to a subset of systems, or are all systems productivity paradigms, making my questions universally applicable? Is there such a thing as a non-productivity paradigm? Can a system ever become a system if it doesn't follow some sort of life-span arch? Is productivity, as I suspect it is, a perquisite for the existence and persistence of a system?

Lets assume it is. Now what? How can we extend this assumption in order to acquire something salient to say about a system?

Cognition Is (and isn't):

What is really going on in cognition, thinking, intelligence, processing?

At base cognition is two things:

1. Physical storage of an abstraction

2. Processing across that abstraction

Key to an understanding of cognition of any kind is persistence. An abstraction must be physical and it must be stable. In this case, stability means, at minimum, the structural resistance necessary to allow processing without that processing undoly changing the data's original order or structural layout.

The causal constraints and limits of both systems, abstraction and processing, must work such that neither prohibits or destroys the other.

Riding on top of this abstraction storage/processing dance is the necessity of a cognition system to be energy agnostic with regard to syntactic mapping. This means that it shouldn't take more energy to store and process the string "I ate my lunch" than it takes to store and process the string, "I ate my house".

Syntactic mapping (abstraction storage) and walking those maps (abstraction processing) must be energy agnostic. The abstraction space must be topologically flat with respect to the energy necessary to both store and process.

Thermodynamically, such a system, allows maximum variability and novelty at minimum cost.

What if's… playing out, at a safe distance, simulations, virtualizations of events and situations which would, in actuality, result in huge and direct consequences, is the great advantage of any abstraction system. A powerful cognition system is one that can propagate endless variations on a theme, and do so at low energy cost.

And yet. And yet… syntactical topological flatness carries its own obvious disadvantages. If it takes no more energy to write and read "I ate my house" than it does to write or process the statement, "I ate my lunch", how does one go about measure validity in an abstraction? How does one store and process the very necessary topological inequality that leads to semantic landscapes… to causal distinction?

The flexibility necessary in an optimal syntactic system, topological flatness, works against the validity mapping that makes semantics topologically rugged, that gives an abstraction syntactic fidelity.

This problem is solved by biology, by mind, though learning. Learning is a physical process. As such it is sensitive to the direction of time. Learning is growth. Growth is directional. Growth is additive. Learning takes aggregate structures from any present and builds super-aggragate structures that can be further aggregated in the next moment.

I will go so far as suggesting that definitions of both evolution and complexity are hinged on the some metric of a system to physically abstract salient aspects of the environment in which it is situated. This abstraction might be as complex as experience stored as memory in mind, and it may be as simple as a shape that maximizes (or minimizes) surface area.

A growth system is a system that can not help but to be organized ontologically. A system that is laid up through time is a system that reflects the hierarchy of influence from which its environment is organized. Think of it this way, the strongest forces effecting an environment will overwhelm and wipe out structures based on less energetic forces. Cosmological evolution provides an easy to understand example. The heat and pressure right after the big bang only allow aggregates based on the most powerful forces. Quarks form first, this lowers the temperature and pressure enough for sub atomic particles, then atoms. Once the heat and pressure is low enough, once the environmental energy is less than the relatively weak electrical bonds of chemistry, molecules can precipitate from the atomic soup. The point is that evolved systems (all systems) are morphological ontologies that accurately abstract the energy histories of the environments from which they evolved. The layered grammars that define the shape and structure (and behavior) of any molecule, reflect the energy epochs from which they were formed. This is learning. It is exactly the same phenomenon that produces any abstraction and processing system. Mind and molecule, at least with regard to structure (data) and processing (environment), are the result of identical process, and as a result, will (statistically) represent the energy ontology that is the environment from which they were formed.

It is for this reason that the ontological structure of any growth system is always and necessarily organized semantically. Regardless of domain, if a system grew into existence, an observer can assume overwhelming semantic relevance that differentiates those things that appeared earlier (causally more energetic) from those things that appeared later (causally less energetic).

This is true of all systems. All systems exhibit semantic contingency as a result of growth. Cognition system's included (but not special). The mind (a mind, any mind), is an evolving system. Intelligence evolves over the life span of an individual in the same way that the proclivity towards intelligence evolves over the life-span of the species (or deeper). Evolving systems can not be expressed as equation. If they could, evolution wouldn't be necessary, wouldn't happen. Math-obsessed people have a tendency to confuse the feeling of the concept of pure abstraction with the causal reality of processing (that allows them to experience this confusion).

Just as important, data is only intelligible, (process-able, representative, model, abstraction) if it is made of parts in a specific and stable arrangement to one another. The zeroith law of computation is that information or data or abstraction must be made of physical parts. The crazies who advocate a "pure math" form of mind or information simply sidestep this most important aspect of information. This is why quantum computing is in reality something completely different than the information-as-ether inclination of the duelists and metaphysics nuts. Where it may indeed be true that the universe (any universe) has to, by principle, be describable, abstract-able by self consistent system of logic, that is not the same what's so ever as the claim that the universe IS (purely and only) math.

Logic is an abstraction. As such it needs a physical realm in which to hold its concepts as parts in steady and constant and particular relation to each-other.

My guess is that we confuse the FEELING of math as ethereal and non-corporal pure-concept with the reality which of course necessitates both a physical REPRESENTATION (in neural memory or on paper or chip or disc) and a set of physical PROCESSING MACHINERY to crawl it and perform transforms on it.

What feels like "pure math" only FEELS like anything because of the physicality that is our brains as copular machinery as they represent and process a very physical entity that IS logic.

We make this mistake all day long. When the only access to reality we have is through our abstraction mechanism, we begin to confuse the theater that is processing with that which is being processed and ultimately with that which that which is being processed represents.

Some of the things the mind (any mind) processes are abstractions, stand-ins for other external objects and processes. Other things the mind processes only and ever exist in the mind. But that doesn't make them any less physical. Alfred Korzybski is famous for declaring truthfully, "The map is not the territory!" But this statement is not logically similar to the false declaration, "The map is not territory!". Abstractions are always and only physical things. The physics of a map, an abstraction system, a language, a grammar, is rarely the same as the physics of the things that map is meant to represent, but the map always obeys and is consistent with some set of physical causal forces and structures built of them.

What one can say is that abstraction systems are either lossy or they aren't useful as abstraction systems. The point of an abstraction is flexibility and processing efficiency. A map of a mountain range could be built out of rocks and made larger than the original it represents. But that would very much defeat the purpose. On the other hand, one is advised to understand that the tradeoff of the flexibility of an effective map is that a great deal of detail has been excluded.

Yet, again and again, we ourselves, as abstraction machines, confuse the all too important difference between representation and what is represented.

Until we get clear on this, any and all attempts at merely squaring up against the problem of machine intelligence will fail.

[more later…]

Randall Reetz

Viewing Our Open Economy Throgh A Closed Economy Lookingglass

And have an idea. Plot the ratio of the total number of dollars in the stock market vs. the total number of dollars in the US economy sans the market (all over the same 100 year history).

My guess is that there have been spikes in this stock vs. M. ratio that correspond with new money entering the market, either shifted from other domestic segments (real-estate, retirement accounts, etc.) or from international influx of investments (rapidly rising wealth of Asia and rest of world, sudden collapse of a large industry, commodity, or governmental or regional stability). Very few of standard economic metrics measure true macro or global interaction between geo-scale segments.

Comparing the Dow Jones against itself over time, or rarely, against other metrics like the GDP is different than reifying this and other comparisons as named metrics in and of themselves. But completely missing is geo-scale metrics that track the shifting values across regions and segments, in effect treating economic entities as markets competing for the maximum percentage of the total global value or geo-M.

What I am getting to is some way to accurately read the total global M and total labor L and total energy use E and to track the motion in real time of the density of these values geographically, or geopolitically, or by production or consumption segment. Once a true global economic sandbox tracker/simulator has been built, we will have the ability to read the economy as it is, in real time, and find the factors that sit at the base of the actual influence hierarchy that drives and causes economic flux.

My suspicion is that actual economic growth is equivalent to, always and only the result of increases in the means and use of tools and infrastructure that can build more for less labor... productivity, and that, in the absence of true growth in productivity, a market responds through acts of trickery which are ultimately not supportable and always culminate in crashes or "adjustments" which tend to pull market values into closer alignment with actual M and productivity values.

When new money comes into a market, standard supply and demand metrics are no longer accurate predictors or models. When new money comes into a market, standard supply and demand metrics will tend to say that the value of a product has risen. In a closed system this evaluation would most often be accurate In an open system, where money can stream into a market not to chase a product for consumption or industry, but just because that market seems a more attractive place in which to speculate than others, it throws the whole system into instability. Producers under such conditions are want to make more shoes, even though people are not growing more feet or walking more holes into their souls. End-consumers begin at inflationary times like these, to speculate with their purchases, buying products and commodities not because they need them, but because it seems foolish not to. Products and commodities take on a currency-like property, and through over-valuation supersaturate the market... leading to an inevitable value crash. If a large enough percentage of an economy's value has been suckered into such a bubble, the crash will bleed over into the economy as a whole... hitting the financial and banking markets first and hardest.

Over the past two decades, three fundamental factors have made large markets in the west especially sensitive and vulnerable to these new-money boom/crash cycles.

First, the undeveloped economies of the world have experienced exponential growth as they adopt tools and infrastructures borrowed from the first world. Importantly, because the third and second world represented the vast majority of the world's population and geography, this explosion in wealth (though still on average only bringing the third world slightly out of poverty) began to represent (by shear size) a larger and larger segment of the global economy. Remember that this "rest of world" economy represents roughly six times the population of the first world. Even smallish changes to a segment of this relative multiple have huge effect on the total global economy. And the actual changes have not been small. Second and third world economies have absolutely exploded! Much of this new money has of course been reinvested into the local economies from which it sprung. But, increasingly, larger and larger chunks of this new money have gone searching for boutique markets like the New York Stock Exchange and its equivalent in Japan, Germany, England, France, and the EU.

The second factor has to do with the paucity of true growth in productivity experienced in western and first world economies during this same twenty or thirty year period. In post-industrial economies, regions that have secure and constant access to reliable transportation of goods and services (shipping, highway, rail, and air transport), ready and secure capital (through investment banking and business and consumer credit), private property ownership (as a means to secure capitalization), education (to steadily feed highly skilled workers into labor markets), and governments that protect and promote the well being and promote the success of their citizens en-mass, and who have built a dependable infrastructure to create, extract, and distribute energy, and the means to grow and process foods cheaply on industrial scales... these rare segments of global marketplace... have had these capabilities for some forty years. Excepting of course for incremental gains made in efficiency of the above systems as a result of new knowledge and tools that result from better understanding of nature through advances in the sciences productivity has largely leveled off and remained level for the better part of a quarter century. What of the computer? you say. Surely the computer and the World Wide Web have had a huge positive impact on first world economies. But interestingly, the net net economic effect of computation and the digital networks it creates, has been surpassingly neutral. We do pump a larger and larger percentage of first world moneys into computation and its infrastructure that consume and use computers.

Real productivity metrics have yet to precipitate outward from the large success of computing as a market and into the larger first world economy. Ironically, the computer industry's success in the west may have impacted second and third world economies the most. It may be that money made in the computing industry flowed more deeply and directly into the rest of world economies where much of the computer industry does its manufacturing, assembly, and customer support. That computing has not resulted in measurable increases in first world productivity has computer industry insiders scratching their heads. During the Dot Com boom, pro-industry annalists creatively sidestepped this uncomfortable truth by inventing the idea of a "new economy" or "cyber economy", famously proclaiming, "The old rules and metrics don't apply". They were wrong, in the short term, but maybe, just maybe, in the long term they will be correct.

I suspect that the true economic benefits or potential benefits caused by the computer and computing upon global productivity have yet to be realized. The computer industry has spent the last 30 years largely learning how to get computers to do what we did (though slower and more awkwardly) before we had computers (writing, printing, telephony, accounting, payroll, data processing, advertising, point of purchase, audio and video broadcast, mathematics, graphing, market tracking and trading, banking, news and reporting, information sharing, post and mail, libraries, process control, etc.). This conversion has been expensive, and time consuming. Much of the time, we have proceeded as an industry (and a society) without a clear goal. Let me restate; neither the computing industry or the consuming public has had a clear idea where computing has been or ought to be going. Much of the time, both industry and market have been happy just to see what new (old) thing the computer can be taught to do... blindly building and consuming our way into the future just because it is "cool" or "fun" or "neat" or adds some strange and intoxicating "immediacy" to our daily lives (even when that immediacy does not equate effectiveness or lead us to deeper and more efficient infrastructure's necessary to cause the kinds of profound increases in productivity we expect from new technology paradigms).

I am a big believer in the future of computing, or the future that computing could build towards, but this belief is contingent upon society getting to a deep clarity of understanding about what computing is and why it matters. We have got to work hard at determining the difference between that which is cool and that which is profound. That which we want and that which will change the world. Until then, we are simply designing and producing towards consumption which will make segments of the industry rich and will bring money from the consuming west into emerging economies, but it will not ultimately support real growth, the kind of growth that is supported by knowledge, tools, and infrastructure that have the capacity to catapult productivity to the next level (the way the tractor pulled plow, germ theory, general education, the steam engine, and electricity have done in the past).

[to be continued...]