Philip Forbes Henshaw  AIA AAAS
ed. 3/9/04,8/12/05, 2/04/06, 4/2/06, 4/25/06 5/14/06 6/23/06 07/02/06 07/04/06 05/18/07 05/25/07 3/20/08

..................................      

..... explorations, work & interests,  big ideas about a little physical worlds
     
 4Dsustainability - Learning Process Design
Consulting to  Developers, Scientists, Engineers & Organizations: 
on
reading the internal design of the evolving  natural systems around you
and inventing creative learning pathways for yourself and them

¸¸¸.·´ ¯ `·.¸¸¸ Notepad For Dummies
How to watch for natural system behavior in ordinary events
 

 

Odd Facts
when you observe a growth or decay curve, odds are you'll find a little natural 'pump' mechanism in its environment making it operate (1)

Research - Concept & Comment - Publication
Bio - Arts, Tools, What interests me
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-- about all kinds of natural systems, little worlds, having internal design and behavior of their own --
--
a main issue now is how the parts of growth systems as they run into natural limits all run into each other, in conflict --

Reading Hint... - Try just browsing for something interesting, this is a long accumulation of variously successful experiments


Regarding: 

A Science of Natural Systems

05/22/08 05/30 05/31 I've had the very unusual privilege of finding a fascinating large gap in the scientific method and an intriguing good way to fill it.   It points toward returning science to being a general purpose tool for anyone with curiosity, a general craft rather than a specialty.   Modern science developed around the model of physics, containing a major flaw: physics did not include a method for studying individual events.  Individual events turn out to have individual differences that confound traditional formulas and statistical description.  What fills the gap is a way of asking a different kind of question, allowing people to understand much more about the individual events they are directly observing by engaging their own observations with a new approach to old ways of 'connecting the dots'.  The main problem has not been a lack of data.   It's been that we ask the wrong kinds of questions for the plentiful kinds of naturally available data.

The largest benefit comes when events can be understood as working by, or as parts of, local accumulative learning processes.  Natural systems are a path-finding behavior that records an accumulative 'exploration' of the paths in their environments and their adaptation to each other.   Environments and natural systems can't be easily defined or represented... so to see them as wholes you need to pay close attention to how the path finding accumulates to 'see' how events are being orchestrated.  System development is like a storm that draws 'energy' from where it is found.   Wherever new paths are developing a model of the past directions taken will not show the paths to be taken in the future.   If you learn to ask the right questions, and watch closely, you'll see what the new directions are much sooner than someone that does not.  You start from common informal terms for the character of change.  

Accumulative original path-making seems to be part of nearly all individual events of interest, and so common experience already gives us a lot of knowledge about them.   What I developed is a systematic way of asking better questions about how these learning paths develop, that can potentially make combining some common knowledge and direct observation much more meaningful.   Some of the key questions are very general, but their long range forecasting potential is huge.  The four basic questions are to a) what observations suggest 'arrows' of change? b) how are the directions they point changing? c) what processes are doing that and what's the environment they are moving through?, and d) what will those processes collide with and how will that change them?   Common experience gives us lots of usable models for this, once you get the idea of watching nature's learning paths at work!    [theoretical note: A 'model' is not the thing you're observing.  It's a 'metaphor', a mental construct serving as a 'tool' to help you learning how to directly engage with your world.   Mathematical models gain their value not from explanation, but as 'metaphors' for nature that engineers and designers use in the process of directly learning how to do things.  Models are nothing without that connection to people using them to discover how to relate to things beyond explanation.  They're 'guides'.]

There start is to use some simple examples of natural systems as a model.   My 'bump on a curve' method discusses a few that can be generalized to help you learn about all natural systems.   'Generalizing' them provides places for ordinary observations to plug in and be used to track where a whole system and its learning process is going.   One kind we all pay great attention to is watching the learning paths that new personal relationships take.   It starts at our first contact with a new friend or new relationship at work and we naturally look for the 'arrows' that point where it might go.  'Arrows' are changes that point to a direction of change, like how readily they respond, or if the kinds of responses branch out or not.  If those arrows start small and change by steadily bigger steps, then you might say the relationship is 'taking off'.  This gives a very concrete meaning to build on the general sense of what 'taking off' means informally.    Then you begin seeing how the other person's responses and the relationship's directions reflect things being discovered in the environment through which the relationship is making its path.   That's a major step, toward having it become 'real' and participate in the world.  

Then there are hazards, like not watching as the relationship alters its own environment, or something else does, and you're not ready to be responsive when you need to be...  They go from 'start' through 'take off' to 'integration', if they avoid embarrassing failure along the way.   For each learning process as a whole the development always goes from beginning to complete end, and always experiences the four basic changes in direction, in one or another form.  ¸¸¸.·´ ¯ `·.¸¸¸    This way of organizing the historical progression of personal relationship issues is just one good way to start thinking of about natural systems as complex relationships that follow a learning process.   This can be defined in rigorous scientific terms or left informal, applied to help understand familiar short or long term learning experiences, as well as ones that take a thousand years to develop like a civilization or a few nanoseconds like the plasma cascade that forms the learning path of a spark or lightning.  All development paths start with an explosion by a process that is then altered by what that explosion runs into.    pfh

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5/16/07..09/08/07 02/19/08  Also Known As..
Natural Systems Theory, General Behavioral Economics, a Unified Physics of Open Systems
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 exploring their own individual emerging internal networks of relationships (the 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 ) 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.

pfh


p.s.

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.


 

Some things to change?:

  • What if politicians tried to impress people with better information about their world rather than promises they can't fulfill?

  • What if online adds a narrow check box bar at the bottom, with one click for Truthful? Y, N  Helpful Y, N?

  • What if Google let you choose what bias to use, the Kids view, the Women's view, the Scientist's view, your Tailored view... a Neutral view...??
     

Concept & Comment:   .....little essays

Odd Facts  back to Top
2/3/08 (1)... and need to decide if you should try to: a) turn it off,  b) get out of it's way, or c) let it go to it's own level of comfort as a partner in your world.
- 2/1/08
Absorbing CO2 produced by average spending w/ trees, add ~1acre of mature forest per $150,000

(links from and to others)

  • Others - who else thinks like I do & a scattering of science links 4/4/06, 5/23/08
 
(ph discussions & posts)
  • Jun 06 on. FriAM - Santa Fe complexity list serve discussion archive
  • Mar 06 NECSI - on capitalism starting 2/06 from R.Newman article in the Guardian
 
(ph sustainable design)
  • 08 HDS my design & research consulting & SD methods intro
 


a Physics for Open Systems

                               Introductions


Other places - (my May 07 bookmarks) LinkWeb,
Other people - [the partial short list]

  • The Howard T. Odum school of general process ecology, Holling, Ulanowicz, Salthe
  • Buckminster Fuller, Lou Kahn, Jane Jacobs, Lester Brown & Worldwatch, Malcolm Gladwell
  • Scientific archeology of NASA, Joseph Tainter, Colin Renfrew, Erich Neumann
  • Evolutionary Developmental Biology, Steven J Gould, D'Arcy Thompson
  • Network science, Self-organizing software devl., Alife, von Neumann, Barabasi
  • General systems theory, Boulding, Bertalanffy, Ashby, Weinberg, Donella Meadows
  • Old guys finding useful errors: Lamarck, Leibniz, Zeno, C.L. Henshaw
  • Others who may think like me: Wendell Berry, Walter Lippman,
  • Numerous close personal relations asking great questions and sharing experience
  • Complexity as learning processes of open environments
  • Sustainability science (when not duplicating the mistakes being corrected...arg!)

Natural Systems  and Emerging Events     
....what happens when things multiply.
 05? 07/04/06 5/26/07 2/2/08 3/3/08

Because natural forms is not ethereal, but a material, it can only come about though a process of development.   It's then possible to locate those processes and explore how they work.  That gives you something like a science of discovery for the origins of natural form    It works!

Every 4 year old child knows that frogs jump because you poke them.  It would be nice if the philosophy of science did not still rely on that idea of causal determinism.   The idea that careful description of how actions of one kind produce effects of another is sufficient 'natural cause' for how we determine our own effects is perfectly sound.   That we say our models of deterministic prediction are also invisibly 'embedded' in the universe as the direct causes of nature is not.   The truth of course, is it's the frog that jumps, not your finger.    Looking at the time lags between stimuli and response, you can actually prove that the jumping of a frog is a local complex system learning process.    A natural systems approach has to do with watching very closely as the 'frog jumps' to observe  how that learning process develops, and identify the emerging organizational networks that are instrumental in the frog's behavior.  It's not about designing an 'artificial frog' as normal control oriented science would be.  It's about discovering how to read the internal processes of the real frog.    There are a number of important discoveries about the true sources of eventfulness and organization in nature to be made, including how a lot of it is 'hidden in sight', disguised by our own unthinking categories.    I have a collection of basic principles:

Key Principles of Natural Systems - Common Sense - Basic Theory - Systems Thinking - Research Methods  12/07

My main collection of work on natural system theory, application, scientific methods, tools and publication is  The Physics of Happening.   I've been slowly building and rebuilding the collection, trying to find the right words as well as productive techniques.   To me, looking at historical records for when, where and how developmental processes changed direction is an obvious short-cut for finding the physical systems involved.   I closely observe time traces to see what's happening, focusing particularly on where events begin and end.  Where things begin and end you find the growth & decay of networks of relationships.   It's 1 to 1.   Physics has looked at nature and asked what universal rules are being followed.   The study of locally emergent systems looks to see what local rules are being developed.    Certainly it may look a little strange to study individual things rather than large classes of similar ones, but it's a key to understanding of the physical world.

  • A notable achievement?  Well there's the development of whole system measures that shows that for economies money does actually directly correspond to energy use.  This has a gigantic implication for economics in that most of our long range economic planning assumes the opposite.  The $shadow principle further shows that the unaccountable energy consumption behind our purchase of goods and services is 10 or more times that of the energy uses that even careful study can identify and account for.  
  • The property of infinitely smooth progression in mathematics is called 'derivative continuity'. It is one of the most fundamental and useful properties of mathematical functions.   Physical systems often display a similar but less well defined property, call it 'flow', or 'natural continuity'.  It characterizes the physical processes and developmental systems that display accumulative proportional change.   It's the main property of nature that formulas attempt to emulate, and the main reason equations are useful.    How that property is used here, though, is sort of the reverse of that, and opens many new ways directly study nature.   The natural continuity of flows is more complex than equations, continually changing as the underlying complex systems continually change.  For example one section of curve might have all derivatives positive and a following section of the curve having all derivatives negative.   That suggests looking for underlying systems and how they switch from growth to decay. 
  • The intent is not aimed at writing a formula. You might use a formula as a way to see how the natural system diverges from it, though.  The intent is to understand how the instrumental systems develop and interact.    Reading shifts in the continuity in natural flows is a big help.   It's an approach that works with any of the different 'schools' of science for how to describe the organization the natural systems being studied. serving greatly to 'getting the problem right' whatever analytical method you use.    One promising method is to us network science , starting with mapping networks of internal sets of  working parts of whole complex natural systems. 
  • My sample studies  use various special mathematical tools to expose the structural details of  physical system flows.   One of these is called "derivative reconstruction" (d.r.), a technique and group of computational methods.   Some of the basic routines use the mathematical definition of derivatives in reverse, to reconstruct the probable dynamics of evolving systems given a series of points representing their change over time .  It doesn't always work, but often does, and often exposes otherwise invisible structures of change, opening a new window on the mechanisms of physical process.  Another of the methods used is called "curvature scale space" (CSS) which originated in the field of computer vision.   It uses repeated smoothing of shapes to distinguish and define those features of shape which are the most robust (slowest to disappear with suppression).  Basically you look for where processes begin and end and try to figure out what is beginning and ending.
  • There is no practical barrier to these new methods having wide and immediately useful application in numerous fields.   There is a conceptual barrier though.   It's not the Western cultural habit to think of time as a process.  We tend to think of it as a location, and so equations with time as a variable as describing a system that itself does not change over time.   The evidence is that all natural systems continually change over time, and that the main events causing change in systems are 'tipping points' at which systems 'out of balance' end up disrupting themselves in some fashion.  Ordinary thinking hides that from us.

Network Science     
....what happens when things connect! 
05/24/07.. 09/09/07

Watch this space....   The NetSci conference in NY in May 07.  The ability to convey highly complex system information and understand the evolution of systems from it is advancing to a useful tool very rapidly.   Great Displays from Manuel Lima's VisualComplexity.   My most recent technical notes on linking Net Sci and Natural Systems theory with Complex Systems engineering.. PICS.htm

  • One of the things that causes the 'power law' distribution of organization in emergent systems appears to be the elaboration and refinement that occurs in how they grow and develop.  

  • One of the ways of understanding how networks are embedded into the complex systems which produce them is hat all the nodes are actually 'hives' of activity in the larger system when looked at at a different scale.  

  • To understand what it means that people are connected by 5 degrees of separation and web pages by 19 is helps to consider how close connection of this kind has a simultaneous reverse property of great isolation and independence.   What the difference between 5 and 19 means is that information is divided into many many more separate worlds, the flip side of the astonishment we all feel when finding out that all our very separate worlds are also quite closely connected.

The tools for displaying these structures and relationships are so good, and the complex system decisions people need to make so pressing, that there clearly should be an office of complex information display in every branch of government