Intro Essays for a Physics of Open Systems
JL Henshaw  1/21/2010   (see also intro heading on main page "
Open Systems Physics" 5/16/07..09/08/07 02/19/08 

a Natural Systems Theory *Definition note

It turns out that when you distinguish individual 'physical events' from individual 'information events' your attention switches between theory and process.  Theory is located in our minds and process in the physical world our theories grapple with.   When you study that difference, you discover that physics is based on describing controlled variables in closed systems, while physical processes emerge within their own open environments with significant degrees of independent local design and behavior.   In short, when studying the uncontrolled physical process systems that we are immersed in, it is very useful to use physics backwards, as a learning tool for opportunistic systems in addition to using it as a descriptive tool for deterministic systems.    Deterministic systems are not what give us our big or our most interesting problems in relating to the world.   

As I progress with finding better words for explaining how to make this insight useful I update this site.   I'm way behind, though, so you should browse for what you find interesting rather than what it appears I find interesting.  To begin to identify where natural systems are, I use a method of watching how they develop.   That leads to learning how to observe individual emerging internal networks of relationships to find a Physics of Happening.    With the 'emergence' of sustainability science, all  the separate sciences are having to shift their explanatory paradigms to functionally connect with each other and the world's stakeholder communities for the business of making the earth sustainable.  

A physical process understanding, recognizing the significantly independent design and behavior of individual natural systems and system events, seems like an obvious starting point.    The learning processes I've developed for that, and have tried to outline here, will hopefully improve and be found useful for that.   We're describing a 'big hill' to climb here, but if you look beyond the climb to where we're really headed it helps a lot.   Oh, and of course, looking 'over the hill' also helps identify the plans for climbing ever steeper hills we seem to have so many of that we urgently need to change!

Finding that your community's 'sacred' truth is flawed is a mixed blessing, of course.  On the plus side, people do leave you to work as you want.   That's not the intent, but it does have benefits.  The scientific problem is to find how to study the deep physics of natural systems from observing individual events, rather than observing large statistical classes of events.  Science has traditionally only done the latter.   Documenting the various 'tricks' for making the opposite approach work is an ongoing effort. 

One of them is studying individual events in relation to simple models, but looking for the discrepancy rather than the fit.  Another is looking for continuities emerging from the noise and reading their sequence and markers (. `.).  What you find is an evolution of individual complex system events that turns out to look surprisingly natural, of course, but also very different, and a break from representing nature with fixed statistical models.   You find many useful new principles for how the local animation of natural system events develops, and how to expand ones own choices.   

There are several other fields of science making good progress on the subject in their areas, systems process ecology, evolutionary development biology, self-organizing control systems engineering, and perhaps others I'm unaware of.     A significant new field of physics, network science, seems particularly successful, studying both theoretical networks and real ones embedded in natural systems.    The networks isolated from real physical systems, and their topology, are unusually helpful in exploring the larger complex systems in which they are embedded.    When you consider the role of 'nodes' in the larger complex system it appears they are typically also 'hives' of, 'grass roots' activity in the larger system.   

That helps you think about the relation between networks and the several other kinds of connection they rely on, and the developmental processes that animate them.    There's a good ways to go, but I think the new outside-in view of the units of natural organization (that has been mine) and the new inside-out view of the same (Net Sci) will connect.   My Chapters model for the timeline of natural system events (from the 'Bump on a Curve' . `. Notepad  for Life's Great Transformative Changes ) shows how one piece of network science fits with my general sequence of evolutionary events and my PICS model discusses it a little further.

That all observable events display the locally evolving workings of complex systems (there's nothing else there) essentially means time is not a location on a scale, but an ongoing universal distributed process.  Add to that the observation that some things begin and end, makes the starting point for the new physics of nature as I approach it.   Consequently all local events individually evolve, and so looking at them as if they occurred in large collections of identical events, with the discrepancies explained away as 'noise', hides how they individually work.   The differences between events help show how they individually develop and ignoring the differences hides how they develop.   Historically, science has relied on a statistical model of nature, and actually missed that the coherence of individual events was being erased by the design of the established method of studying them!  

It takes some effort, but learning how to scientifically study individual events (as opposed to whole classes of events) does let you in on some of the secrets of the things in life that actually matter to you.    Where it begins to yield new secrets of the nature of events is using it to read emerging complex networks.   Growth exposes the things composed of loops of relationships that constitute the insides of natural systems.    Where I started is with the curiously obvious simple idea that if things were too complex to collect good data, you could learn how a local process worked by watching carefully.    That's using the world to imprint directly on your mind.   

Why that is rejected by so many scientists is a real mystery, just learning by unbiased observation.    How to do it is a little tricky perhaps, as it means becoming a good direct observer, to watch what is being invented right were you see it, rather than imagining it as being determined by some myth or formula or something else.   What seems clear is that when this and the other co-evolving work on the subject link together, it will change the meaning of science and nature, enlarging science to include a general study of locally evolving events, .... of what's happening!   

One trick to keep from getting confused is to remember that any new understanding of  nature is of  real things we've been living with all along.    It's also very reasonable to be cautious about unexpected riches, adapting a friend's metaphor, as when a search for 'needles in a haystack' unexpectedly turns out to find the whole haystack made of needles.    You find different things when you ask different questions, and finding a global change in the appearance of the physical world, in this case, should be kept in perspective.   The way to see individual behavior is not to give up your ideal of things following rules.   It's to hold your idea of things following common behaviors and also watch the discrepancies observed in individual events.   

Usually  models have been used to represent and replace nature, explaining the individual differences as 'noise' and the model as what is real.    I experimented with looking through models backwards, essentially, looking through them as an aid to directly observing the real-time individual behaviors of nature themselves.    Just nuts, huh?, well, ...but also highly productive!

People often take special note of the large apparent significance of circumstantial events, the 'butterfly effect', etc.   What's easily overlooked is that these are events that the environment responds to with large changes, and that it's the environment that is doing all the work of producing the big effect.   No doubt some individual causes have great individual influence.  Quite often the hidden 'ripe circumstance', that was quite invisible up to that point, clearly had the larger part of cause.    It's not that it's not relevant to consider individual external events, it's that the path of learning about what causes them to have effect is elsewhere.  There's also the strange feature of systems you could call 'causation from all over'.  This is often called 'heterarchy'.   

What explains what overcomes the impossibility of running a sensible world with either deterministic 'butterflies' or 'causes from all over' is that natural systems succeed by being opportunistic not deterministic.   Natural systems 'explore' their worlds and go where they find openings.   That's only slightly stretches the language, and is what I observe as best fitting the evidence, keeping a simple model in hand and carefully watching the discrepancies.    The deep model I use as my own reference and exploratory guide is organizational continuity.    It takes a process to change.   It's a really wonderful tool for reading the meaning of events, pointing out where change is developing, or where change is missing, what kind of evolving process networks to look for.



Of course, we all make mistakes.... and I'm not immune, but I also see some big ones being made.     The truth is that bad news is the very best kind of news you can possibly get, if it comes in time.   The compensation is partly that nature never leaves a careful observer without a silver lining.  Steering a course in nature is partly a matter of looking out for the really really bad news.   When you get the knack you find it's not only much safer, but also lots of fun.

Take the hope of  protecting the future of the earth with 'sustainable design' for economic development, and technology and lifestyle changes to mitigate global warming.  You'd think these world wide efforts would be well thought out by someone, wouldn't you?     Asking the right question, though, makes it's fairly easy to prove that the broad consensus approach to these very laudable goals provides only temporary symptomatic relief and will make the underlying problem we have far worse.   They're both plans to relieve symptoms and let the underlying problem to continue to multiply, i.e. our multiplying exploitation of the earth.

Growth is a creative learning process of a whole system.  Learning is always a challenge to overcome complexity that either stabilizes or destabilizes.    If a teacher gives out more and more homework until the students go completely mad, it's not helping them learn.   That's what we're in the process of doing to ourselves with our plan for endless economic growth.   Understanding the multiplying impacts of economic growth and the complex responses of environments, then inventing how to adapt to them, and coordinate our responses, are all necessarily parts of the contract.   The 'genius in the back row' says 'how about we have less homework instead of more'.  

Once the indispensable technologies beginning to approach their limits and the impacts of hitting the limits begins to multiply, the complexity of accommodating change multiplies too, as the coordination of responses also becomes increasingly urgent, conflicted and delayed.   The central reason is the old tricks stop working, because as you begin to rely on efficiencies they develop more and more slowly, not more and more rapidly.   That's both a valid interpretation of thermodynamics for natural systems and what's observably happening.

The error in the new effort to save the earth and while accommodating continual economic growth is that just as the whole system is turning to rely on efficiencies to reduce our impacts on the earth's natural systems, they're inherently running out.      It may not be predictable when that would lead to mass confusion and failure, but it's certain that it would.   Our ignorance of just where we'd loose control of responding to the multiplying impacts of growth is not made any safer by the fact that rushing at the limits without knowing where they are is just what we've been doing all along, of course, even if in the past we seemed to get away with it!    Growth systems either stabilize or destabilize, a simple direct new understanding coming from recognizing growth events as local learning processes of individual natural network systems.  A growth system, like modern civilization, may choose when but not whether to change, and that begins a very interesting new kind of discussion.


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