Category Archives: Pattern Language

“Dual paradigm view” Can ecosystems be stable?

Reposting a November 25, 2014 at 4:46 pm comment to Quanta on the Tracy-Widom New Universal Law article

This is a simple way to demonstrate the “dual paradigm view” as a bridge between the abstract complex systems theory and direct study of individual complex systems, to advance our understanding to of the mysterious phenomenon of “emergence”.  The article suggested that as statistical systems ecologies generally could never be structurally stable, but did not compare that to systems that rely of “accumulative organizational design” particularly those with “learning parts” as ecosystems systems so often to have rather than “correlated random variables”.   The moderator clearly liked this better than my first response not published.  

The “dual paradigm view” addresses the dilemma of complexity science that computer models are fine for theory, but don’t really let you study nature.  That’s what a way to connect mathematical systems theory with individual systems study addresses.   Much of my work of the past 35 years has been on that subject, now recently raised by David Pines’ in a founder’s article for SFRI Emergence: A unifying theme for 21st century science, saying that physics and complex systems science now need a way to study the physical phenomenon of emergence and actual complex systems to progress.   My reply to his article  Can Physics Study Behavior not Theory, was first posted on Medium.

It’s interesting that with such a number of cross connecting areas of physics being discussed, the ultimate finding technically didn’t answer the initial question posed. That was Robert May’s “question about whether a complex ecosystem can ever be stable, or whether interactions between species inevitably lead some to wipe out others”.

The mathematical analysis of that question and others was limited to “kinds of random growth” and “systems of correlated random variables”. There are also lots of non-randomly behaving systems too is worth considering, and may have been overlooked in answering the basic question. The variety of organizational growth systems that are familiar everywhere in nature display many kinds of growth curves and outcomes, often having an overall appearance of being 1) quite lopsided, 2) quite symmetric, or 3) reaching extended stable states.

note: How the meaning of probability distribution curve shapes (as discussed in the article) differs from the meaning of these individual development curve shapes was skipped in this short comment on the article.  Please do bring it up of course if needed.   The question posed was about the development of individual ecosystems, and their potential structural stability.

 Generic common curve shapes
for the development of organizational systems.

We probably know of lots familiar examples of these from personal experience, where the systems involved are going through progressive organizational change during their periods of acceleration or deceleration. Reversals in curvature don’t always reflect systemic changes in direction for organizational development, but often do though (shown as gaps in the diagram for raising those questions).

The one looking like a TW distribution curve is familiar to all economics and other matters, as a “meteoric rise” followed by “immediate decline”, like many a seemingly fine business plans might experience. The quite unusual thing is this same shape turns up in Gamma Ray burst records too (see image of BATSE 551 #1 below). It raises the question of whether that system (presumably of radiation from black hole collapse) reflects the organizational stages of a system that experiences a “blows out” (like some of our best business plans do) or that of a statistical distribution for correlated variables, or something else?

In any case, just asking that raises the possibility of a bridge between TW correlations and the fates of natural system organization designs, and perhaps a need to consider whether the other kinds of system are available to change the outcome for May’s ecosystems, depending on their design.

Gamma Ray Burst “BATSE 551 #1”  – Raw data dynamically smoothed.

( reposted from the Pattern Language Debategraph

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jlh

Can science learn to read “pattern language”…?

This post is a section of my report titled “Approaching 30 days from the 40th Anniversary” on attending the quite exciting 2012 40th Anniversary meeting on the Meadows and Randers authored Club of Rome “Limits to Growth” study.   The excerpt is on the deep reasons why the science, as solid as it still seems to be, isn’t widely accepted.    Science is still struggling to find a comfortable way to discuss natural systems whose innovative systems are housed internally, and so largely hidden from view.

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I think the real reason the public as well as most of the scientific community is largely ignoring the rather well established hard limits to growth, is that it presents the scientific community a new problem it hasn’t yet learn how to deal with.   It has yet to find a good way to make sense of self-designing and self-managing systems, like weather systems, cultures and economies, that have working designs that are  hidden internally, displaying organization much too complex and localized to be determined by external forces.

Science is built around identifying how one thing controls another,
not how to study the  patterns of uncontrolled systems and how they became designed to work by themselves.

So science is naturally somewhat lost in discussing how they work, having no model for what are better described as “opportunistic” than “deterministic” systems.   Though both climate and economies display highly inventive systems, they do still necessarily operate within what traditional science can define as their natural bounds.     Climate is still fundamentally a complex pressure-temperature behavior, of unchanging deterministic processes following fixed laws of science.

Economies though, are able to be far more creative, and move the boundaries of what is possible by innovative design, much further than the push and pull forces of the weather can.   It has given traditional science very little to anchor reliable theory on, except as in the Limits to Growth study, fixing boundary conditions and experimenting with multiple options.  Still, because economies do display deeply creative behavior, constantly inventing new ways to use energy as a normal rule, that natural science still lacks a widely accepted way to study them as natural systems, adds uncertainty for others to what anyone might say about them.

Constantly inventing new organization is just what natural systems ‘do’.   It lets economies as well as ecologies create new kinds of organization and uses for their energy resources, making formerly useless things highly profitable often enough.   Using the profits as returns on energy investment to grow by building more innovations.   It’s complicated by not being a ‘numeric’ process, though we can see it through our measures.   It’s an “organizational process”, of fitting complementary parts together, more amenable to study as a “pattern language” of “design elements” than equations.

The rigid limits of any mode of productivity still do exist, of course, but as limits of the organizational processes science has yet to find a way to study.   Those limits are still determined by the earth and the organization of the internal and external systems that any innovation depends on, but with each new innovation there are new unknown limits.  It leaves a stubborn problem for traditional scientific prediction.   What seems to work better is a language of observing such systems to see when their own organization is being stretched.

Natural systems generally link individual units of organization in an open rather than deterministic environment, each with its own internal organization that emerged during its own development, creating a serious mis-match between the natural design and the information an observer could collect, and with the kinds of behaviors that can be emulated by equations.

That big problem for science also makes a big and very fascinating subject of study, that science has quite generally not realized is there, having avoided the study of self-designing and managing systems in general.    Self-designing ans managing systems not only seem to develop by themselves, but to have their “works” hidden internally within the boundaries of their design, as an individual system maintaining internal organization for responding to external systems, like we see in living systems as a special case in point.   Continue reading Can science learn to read “pattern language”…?