It follows nature’s model of systems design to begin the growth of any system with a business model for multiplying one’s control of their environment. That’s what happens when planting a seed, that grows by multiplying it’s ‘secret’ internal design, consuming its host environment ever faster, at first. It doesn’t pay in the end, though, for either businesses or any other kind of economic system, to keep following that model, as if endlessly getting nature ever more pregnant could be the soul (or ‘sole’) purpose of self-organization.
When you get environments pregnant you also need to budget for child care, is the point. That’s the time a growth system stops using its profits for its own self-inflation, and switches to using them instead for discovering its original purposes and nurturing them. Study any kind of growth system that fulfills its own purposes. That’s what is done to discover and fulfill their ultimate purposes.
I’ve written extensively, from numerous perspectives, on both the systems science and financial implications. What’s implied is our need to follow nature’s example, and instead of investing in self-inflation to consuming our host ever faster…, giving away our profits to find our true purposes in having begun to grow.
Getting the whole system to reorient its purposes, from growth to funding what matters to us… would indeed involve some “rethinking”. It might be easier than it seems at first, though, as it seems to be for lots of other kinds of systems in nature that do it casually and simply, without a thought actually. They often succeed by just giving all their products away to see what others make use of. That’s what the cells in organisms and the organisms in ecologies largely do. They don’t give away what is needed for them to operate, though, so there’s some sort of line between what they must give away for the whole to thrive, and must keep for themselves to thrive.
Knowing that it’s probably a physical necessity for our survival makes it easy to discard the options that obviously wouldn’t work, and send you “back to the drawing board” looking for the secret to the ones that would…
A way to respond to experience we’re unable to articulate.
There are lots of cases when what attracts us to a theory is its sort of “spooky” truth. “Urban myths” often contain them, and science can often be the source of them, as well as cultural sayings and religion too, of course. The value is that they give you, a way to respond to experience we’re unable to articulate.
For applying them to real world problems, however, it’s rather important to “do the work” of finding real examples you can study and articulate. What’s NOT needed is “spooky action” for real problems… ;-) So here are a couple notes on how to find real examples to help you apply curiously attractive metaphors and “spooky theories” to decision making about the real problems, such as our groping with finding our place on earth. jlh
“spooky theory” then becomes a metaphor for something real you understand well enough to use as a guide.
1. for Greenleap 9/23/12 – “Spooky biomimicry” as “what to do”
Richard – Ultimately “what to do” is a communal process somehow, as we’re in communal trouble. Lots of people are seeking new directions of learning, but I can tell are often still using the blinders of the past to guide them… and not wanting to hear about it at all. All you can offer them a more authentic way to search for new learning, hoping they’ll see it as fun.
Natural systems are the complexly organized and behaving “creatures of nature” that by definition operate without our thinking about them, or knowing anything about them, or doing anything, and are largely invisible to us. That’s by definition “spooky nature”. It’s also the source of all our mysterious stories about unanswered questions, and all our mysterious experiences. What we can do with “spooky ideas” that situations suggest to us is then find an example that isn’t spooky, that we can then use as a real guide to how complex systems work and how to interact with them. – ed jlh
Preface: My last post on the dramatic declining share of wages in GDP since 1970 mostly discussed that remarkable change in behavior of the whole system in relation to how the numbing complexity of business would make computers better “wage earners”, shifting income from wage earners to investors. Complexity too great to follow what’s happening… ??The graph here is a simpler version, showing the same dramatic shift in the disproportionate changes in wages and GDP since 1970.
This post is on how the same shift from wages to profits reduces demand for the products, “made for people” but for which neither business decision making tools nor investors have an appetite. The economy visibly changed behavior. It was coincident with computer decision making emerging as a leading tool of business, and the historic numbing complexity everyone has experienced (reflected in changing language use).
The third important way is a later realization. Computers are overwhelmingly better at making deterministic predictions… but can’t be programmed to consider human values, so they’re omitted from the rules for what to optimize… Computers are even more likely to keep applying old values that no longer apply than humans too. When resource prices go up, for example, the old standard investment models say “speed up”, while nature is signaling “slow down”.
It may seem there’s nothing more dispassionate and “neutral” than automated decision making, but that easily becomes purely ruthless too. So it seems to create a “perfect storm” of misdirection to use computers to multiply their programs in a time of fundamental change in our world. If the model says “choice A = X profit” there’s no way to tell if a different story would be told had humans studied how ‘A’ applied in the current circumstance, so the model built without human values also omits any way to argue with it.
You can see one global effect of this naturally “inhuman” decision making of computer models in their universal penny shaving for profit. That seems directly behind the ever stricter control of decisions, since computers were introduce, by the computer’s measure of value, “the bottom line”. Before that, business people needed to think of the business as a whole, and not a single number, ruling almost every choice. So it produces ever growing pressure to “make money” for the sake of money, whether making a bit less to invest in other values might be a better fiduciary choice.
Author’s Note: 2/16 – My work on this problem dates back to the 70’s really, and my developing methods for “whole system accounting”. In simple terms “whole system” or “inclusive” accounting means you can’t keep “robbing Peter to pay Paul” without noticing. It comes from the customary methods of natural science, not used in economics. Instead of using arbitrary accounting categories, one uses naturally defined partitions of the whole system to define your categories. One is ultimately forced to get it right by there being lots of natural reasons you can’t keep “robbing Peter” (calling what’s unaccounted for ‘externalities’) without dire consequences.
Whole system accounting models force you to look at what you are leaving out of the model, by requiring the use of accounting categories that add up to the whole, partitions of the system. That’s what natural science does to validate the data collection and produce “closed accounting” of the system in question. Oddly so do business financial accounts, but just not economic accounts. Using partitions of the whole for your accounting categories forces you to estimate how much is going uncounted. The first discussions of complete economic economic models of that kind are my 1983 General Allocation Theory and 1985 Unconditional Positive feedback in the economic system in the SGSR proceedings for that year.
1970 marked the sudden end of steadily growing US wages, as a sharply accelerating trend of growing economic inequity and loss or resilience began.
“Information overload” was a rapidly growing topic of conversation and computers emerged as the premiere tool for driving business profit.
Was that how humans began to be replaced by technology,
as things got too complex?
I think the question is quite relevant, and in line with Nobel laureate Wassily Leontief’s 1983 warning that humans will go the way of the horse in the business of providing goods and services. What most people don’t know is that started dramatically in ~1970,
It’s remarkably clear in the data, quite indelible as a “coincidence” between introducing computers for business use in ~1970 and the “the great divergence” of breaking American society apart with lagging earnings from employment and multiplying earnings from wealth. Why did it occur. Following from my 2010 Complexity too great to follow what’s happening… ?one could explain it as cause by the numbing increase in the complexity of everything we do, affecting people but not the computers or the calculation of profits. Looked at from a social view of ever faster increasing economic inequity… it looks more like people using computers to make money, robbing Peter to pay Paul and not counting it.
For those interested, here’s the same data without indexing the wage curves to GDP:
On now to recognize the somewhat universal responses to system and relationship overload, as strains resulting in loss of resilience and a risk of sudden disruption; replying to Helene on Systems Thinking World on her “UN Call for Revolutionary Thinking” thread.
The most general pattern is resilient relationships becoming rigid, like the surface of a balloon does *before* it can be easily pricked by a pin, or as people become rigid before losing patience. I think that comes directly from resilient systems generally being organized as networks of things that share their resources, and when all the parts run out of spare capacities to share at once the system can’t be flexible, and is then vulnerable to sudden failure.
@Helene – Thanks for the reminder. Here are some principles for detecting and responding to the inflection point. Mathematically it’s “passing it’s point of diminishing returns”, when increasing benefit of expansion starts to decrease. Long successful habits of expanding a system become a liability, and strain their internal parts and environments.
It means about the same thing for a whole economy as for a little girl outgrowing her only party dress. Ignoring strain on one’s limits brings an unexpected end to the parties. The problem for systems operated by abstract rules of thinking, is that responding to change isn’t in the rules. So there’s a need to revive common metaphors for responding to the unknown, like for “overdoing it” or “crossing the line”, as strategic signs of externalities needing close examination.
The most common signs of “overdoing it”, and needing new strategy, are formerly stable and flexible sub-systems
developing “the shakes” or “become rigid”