An interesting global question is, to me, raised by Ernst Ising’s work in physics – (see the arxiv pre-print on his life and work if interested. https://arxiv.org/pdf/1706.01764.pdf)
Ising’s main work in the 1920’s was deriving a mathematical explanation for ferromagnetism, the ability of atoms in certain solid metals to develop aligned spins, and exhibit permanent magnetic fields in there surroundings as a result. The part of that might be of interest from a pattern science viewpoint is how his model has been successfully applied to numerous collective phenomena, both other emergent collective atomic behaviors like magnetism as well as emergent collective macroscopic behaviors like the emergence of organization in crowds.
The math, honestly, is beyond me, but there’s an interesting assumption in the work that might be discussed from a pattern science perspective, that the math rests on treating such phenomena as arising from purely local interactions.
“So, if we do not assume [ ] that [ ] quite distant elements exert an
inﬂuence on each other [ ] we do not succeed in explaining
ferromagnetism from our assumptions. It is [thus] to be expected
that this assertion also holds true for a spatial model in which only
elements in the nearby environment interact with each other.”
What I suspect is that there’s more of a wave/partical type duality present, involving both local and contextual interaction
in bringing about collective organization.
In the collective phenomena we observe there is certainly has a strong local character, whether it’s snowflake formation, ecologies, social movements or probably also the punctuated equilibria of emerging species. All such collective phenomena seem to arise in relatively small centers and then spread mysteriously. They also seem to require specially primed and fertile environments, as global conditions that are receptive to the local accumulation of collective designs.
So my question is who else is talking about this pattern of nature. Is this raised in Christopher Alexander’s “The Nature of Order” or other pattern language writings? Is it raised in the work of anyone else writing in the pattern language field? More specifically, does it need to be understood to know how to describe the contexts we work in, perhaps such that a calm and receptive and so fertile context is needed to be a good host for pattern designs to flourish?
A change in natural science is emerging along with “computing”
turning away from using theory & equations as a guide,
toward using data pattern recognition for
naturally occurring systems revealed in the data to be a guide.
Note: About 20 Years ago algorithms were developed for selectively extracting differentiable continuities from raw data, making a major step beyond “splines” for true mining of natural continuities from noisy data without regression. The result was quite successful forensic pattern recognition of discovered natural systems, their forms and behaviors. Combined with a general systems “pattern language” based only on the constraint of energy conservation, that pattern mining has provided a very productive alternative to AI for investigating naturally occurring forms and designs. The one unusual leap for applying scientific methods was to use it to capture the great richness of natural textures available from studying uniquely individual cases and forms found in nature. That is what overcomes the worst faults of studying individual cases, and so instead greatly enriches theory with directly observed phenomenology. The rudimentary tools successfully developed have been proven useful again and again with subjects such as illustrated below. 10/21/16
A long central principle of modern science, relying on defining nature with the information we can find, is considered here by way of eight examples of how important it is for science to also rely on doing the opposite, looking for patterns in the information we are missing somehow. Doing much the reverse lets us use the information we have to ask better questions about what nature is hiding from us.
It’s such an odd and obvious mistake to stubbornly treat nature as our data, as Neils Bohr and Popper insisted on and the QM community has maintained. Being limited to analysis and data creates a large blind spot for science, made unable by that limitation to learn from observation, and to see clearly how very different the “data world” (what fits in a computer) is from the “material world” (what doesn’t). The puzzles of found in natural patterns, turning up in ‘bigdata and various pattern sciences seems to be putting all of these matters into question again.
So I may take some unfair advantage, perhaps, by making a little fun of that prior arbitrary constraint on scientific inquiry, insisting that nothing we have no data for can exist. That of course is almost everything when it comes down to. It’s no joke, though, that our data is decidedly inferior for defining nature. Here and elsewhere I tend to allow that nature defines itself, as I certainly don’t do it.
The “Impacts Uncounted” article mentioned describes a simply enormous worldwide neglect in economic accounting, a huge mismeasure of lasting business environmental impacts. It’s caused by the traditional insistence on trusting the data at hand and refusal to look for what data is going uncounted, as if the fact that we can only study the data we have means nature is not being misrepresented by it, a curiously deep concern for understanding the scientific method. In reality there is more to life than the data we have. Treating “science” as whatever our data defines, then, actually means “flying blind” regarding all the kinds and scales of phenomena going unmeasured, the difference between nature and data going unseen. For accurate accounting, even older scientific principles need to apply, such as defining units of measure in relation to the whole system or “universe” for that measure, not just the part easy to measure, and so “Impacts Uncounted” is the effect of counting the global impacts of business using local measures, as is today standard around the world, a big mistake.
So these 8 examples are “data visualizations” that neatly expose where important data is very much missing, as a guide to where to go and look. Those hiding places exposed as gaps in the data turn our attention to phenomena of perhaps another kind or another scale, or on another plane with material influence perhaps. That is then what needs to be discovered and looked into. to really understand what the measures display and the systems or events they refer to. That the data available, then, always points to phenomena beyond the scope of the data to define is both the oldest and perhaps now the newest of deep scientific principles for interpreting what we see.
Is science coming full circle…? The answer seems to be YES!
Persistent patterns in data generally reflect complex natural forms of design, complex and complicated well beyond what data can define. So we present data in a way to helps show someone what’s missing.
Data from a natural source is generally biased and incomplete as a result of how it’s collected, and a “proxy” for various things other than what it is said to measure. So not really knowing what it measures, it is best studied as being another way of sampling an undefined universe, to become meaningful by discovering its boundaries
Patrick Ball’s HRDAG methods demonstrate comparing sources for death records in conflict environments, using the differences and overlaps to reveal the true totals. My own research shows environmental impacts of business are undefined, lacking a common denominator to make them comparable as shares of the same universe. Correcting the mismeasure appears to increase the impact scale of business by several orders of magnitude. In both cases characterizing the universe the original data is implicitly sampled from serves as common denominator for making the original data meaningful.
For discussing basic explanatory principles of physics used for forensic systems research
1. See where hidden connecting events shifted the flows??
To help people understand my work here are a couple examples of data science to discover dramatic recent culture changes in New York City. The work is based on a careful lifelong study of eventful natural change, of all sorts, done by following the stages of growth and decay evident in the natural life-cycles of culture change events.
following the stages of growth and decay evident in the natural life-cycles of culture change
My method depends on finding data that shows clear evidence of growth or decay, as those identify natural processes of irreversible organizational development, in the natural successions of change. Below are samples from two advanced studies of unexpected dramatic societal change, and a drawing of the markers of change I use to suggest what evidence to look for to discover what’s changing.
The two advanced studies are the mysterious 1991 collapse of the great NYC crack culture (1), and second the mysterious 1970 splitting apart of the US economy into rich and poor sectors on different tracks (2). Both were simply enormous cultural events that very largely went unnoticed, dramatic “break-outs” of culture change that had been brewing for a long time, and then swiftly changed how we live. The study of the collapse of the NYC crack culture and many other examples are in the archive of my research from the 80’s and 90’s called “The physics of happening”
It gets easier to discuss these culture changes once you sense what is being opened up to view is really the stories of our own lives. These and patterns of change in things we are all talking about anyway, only with data showing the systematic progression of key measurements of them. The basic science for following markers of change, implied by the physics principle of energy conservation (3), implying that lasting change is a process of organizational development. So the markers suggest places to ask “what’s developing”.
basic science for following markers of organization change, implied by the physics principle of energy conservation
the markers suggesting places to ask “what’s developing”.
… three years before the mayor who took credit for it took office. The real main player was the strain on the families of the NYC drug cultures involved. They had become particularly traumatized by it, and the rest of society desperately searching for some way to change too. Everything people wanted to have work started working all at once, when their kids stopped looking up to the drug lords! They turned to the emerging Hip-Hop mass culture as an exciting alternative to be part of, a riveting story when well told.
What tipped me off was the “decay curve” shape of the NYS murder rate data shown in the NY Times. The abrupt decay curve shape, rapid at first and decelerating over years, without wiggle, is a very clear indicator of a the death of a natural culture, in this case seeming to be from the youth that had once fed it turning away. Continue reading NYC data Science… examples→
UN meetings on the first year of SDG implementation are over now, were very intense, and in the end quite successful for finding a new way to discuss the neglected issue of natural limits. The scientific community that understands the connection between our natural limits and economic growth has been totally shut out of the UN discussion for years. I didn’t get to speak to the main body on that directly, but I finally found a way to talk about the problem, that the SDG’s don’t in any real way count the global impacts of our decisions:
The ISO’s world environmental accounting standards fail to honor its fiduciary duty to our interests and human right to honest data,
only counting local impacts, leaving all global impacts of financial decisions uncounted and unaccountable.
SD decision makers are the most hurt, kept from knowing most of what they are deciding.
The 17 Goals
It had seemed I would have a chance to speak at the UN, officially representing the long neglected interests of the scientific community that understands the coupling of the economy and natural limits. Below is the email I sent a number of scientists and other experts who understanding is not being represented:
I found a way for scientists who have long understood natural limits, to get official representation at the UN, in the UN’s community of CSO’s (Civil Society Organizations), as a member of its “Major Groups and Other Stakeholders” (MGoS). The present work is the review and guidance of the UN’s global Sustainable Development Goals project (SDG’s), and the High Level Political Forum’s (HLPF) oversight of it. https://sustainabledevelopment.un.org/hlpf
Please circulate widely. Non-expert members welcome too. There is no organization at this time, just me seeing an opportunity to have our long neglected interests given official recognition. I might start a Google Group with the names or something… Any statement would be in the interests of the group rather than as if representing a group position
The draft text for representing the group’s interest to the UN is is here.
Time was too short for it to get around, and response was slow, except for the two great ones I really appreciate getting, so I turned off the Google invitation form . It still seems to be something that community really should find a way to do though!
The following is written for circulation in the “data science” research communities, on some advances in scientific methods of system recognition I’d like to share. It starts with mention of the very nice 9 year old work published by Google on “Detecting Influenza Epidemics using search engine query data” taken from a letter to that paper’s authors. Take the reference to be to your own work, though, as it involves system recognition either in life or exposed by streams of incoming data.
I expect a lot of new work has followed your seminal paper on detecting epidemics as natural systems.
But are there people starting to focus on more general “system recognition”,
studying “shapes of data” that expose “design patterns” for the systems producing it?
Any individual “epidemic” is a bit like a fire running it’s course, and sometimes innovating the way it spreads. That change in focus directs attention to how epidemics operate as emergent growth systems, with sometimes shifting designs that may be important and discoverable, if you ask the right questions. You sometimes hear doctors talking about them that way. In most fields there may be no one thinking like doctors, even though in a changing world it really would apply to any kind of naturally changing system.
Turning the focus to the systems helps one discover transformations taking place, exposed in data of all sorts. One technique allows data curves to be made differentiable, without distortion. That lets you display evidence of underlying systems perhaps entering periods of convergence, divergence or oscillation, for example, prompting questions about what evidence would confirm it or hint at how and why.
Focusing on “the system” uses “data” as a “proxy” for the systems producing it, like using a differentiable “data equation” to closely examine a system’s natural behavior. In the past we would have substituted a statistic or an equation instead. By prompting better questions that way it makes data more meaningful, whether you find answers right away or not. I think over the years I’ve made quite a lot of progress, with new methods and recognized data signatures for recurrent patterns, and would like to find how to share it with IT, and collaborate on some research.
Where it came from is very briefly summarized with a few links below. Another quick overview is in 16 recent Tweets that got a lot of attention this past weekend, collected as an overview of concepts for reading living systems with bigdata.
I hope to find research groups I can contribute to. If you’re interested you might look at my consulting resume too. If you have questions and want to talk by phone or Skype please just email a suggested time.
Working with BigData, especially learning how to read the designs and behavioral patterns of the earth’s natural systems, its living cultures of all kinds, and to sense our roles in them, opens up a tremendous new field of understanding. It of course also opens up very new kinds of perspectives to puzzle over, both offering to show us new paths and making it clear various reasons to question what we’ve been doing.
This series of Tweets came out in a group somehow, mostly in this sequence today, seeming to build a framework of interconnecting points, like tent stakes and poles maybe, a design for hosting ways to do it. ……Jessie
What we talk about becomes society’s reality, so we can read #BigData for what’s happening #following_all_cultures and #resources_on_earth.
And what may matter most in #BigData is going from reading abstract patterns to reading naturally occurring ones. http://synapse9.com/jlhCRes.pdf
Then add the magic of learning to read the patterns #BigData reveals, as exposing the designs of the natural systems producing it.
Reading #BigData for natural patterns shows you even the best data doesn’t show what systems are producing it.
No degree in #data_science will neglect pattern recognition for understanding the natural systems creating the data.http://www.synapse9.com/pub/2015_PURPLSOC-JLHfinalpub.pdf
If our world #economy is causing trouble for the #earth, why do we think it helps to speed it up? #Get_real_people!
Are @google, @IBM or other #BigData #research teams learning how to read design patterns of natural systems?? http://synapse9.com/jlhCRes.pdf
To start reading natural systems in #bigdata look for cultures made individually, clustering or growing from seeds.
Then follow recognizing nature’s cultures with learning from them, going back and forth between models
When reading #bigdata for behaviors of cultures also note contradictions in the news, like #jobs_going_to_Mexico and #refugees_escaping_too.
#BigData exposes surprising whole system views too, #professionals managing systems of growing inequity, disruptive change and impacts too.
#BigData reveals living cultures: business, economic, social, biological or ecological, etc. all either: homeless, home seeking or enjoying.
As you see their forms you realize two things:1) our world is very #alive and 2) most #bigdata is too “big”, making you look for other views
To read #bigdata as views of shifting cultures, alone or together, pushes a #whole_system_view for units of measure. http://synapse9.com/signals/2014/02/26/whats-scope-4-and-why-all-the-tiers/
A #whole_system_view, like #studying_the_camera not what’s in its view, is how to start seeing ourselves in the data!http://www.synapse9.com/jlhpub.htm#ns
Sixteen Tweets on reading our world in #BigData, it’s many moving parts, units of measure & big recognitions required.
ed note: One tweet, that became #11, was rephrased and put in a more logical location a few hours after the first posting.
Individual organizations, Complex natural designs, Emergent forms of naturally occurring design,
Evolving organization & behavior of complex whole systems,
Discovering more and more of the hidden interior designs of lively whole systems…
One way of introducing the “what” and “how” comes from a “pattern language approach” to the science of “naturally occurring systems”, presented in a paper for PURPLSOC:
Guiding Patterns of Naturally Occurring Design: Elements
that I presented at the July 3-5 PURPLSOC pattern language research meeting in Krems Austria. It was in a group of papers on pattern language as a general science; with papers by Helene Finidori, Helmut Leitner,Takashi Iba Et. All.; Christian Aspalter & Reinhard Bauer. (links to follow)
As an approach to working with natural systems “Guiding Patterns of Naturally Occurring Design: Elements” seems unprecedented in using a fully scientific method for focusing on the “objects of nature”, using a pattern language approach to identify working complex relationships of natural designs, in their natural contexts, with nothing “held equal” or represented with models, a practical way to relate to the “things themselves”, as “known unknowns”.
The key is not to avoid data and models. It’s not to rely to heavily on them. It’s to just never use them to represent natural systems, but only to help you discover why naturally occurring systems and their complex designs are of real interest, and doing things quite different from theory. It turns out that Christopher Alexander’s pattern language, as a structured language for discussing holistic solutions, as designs for recurrent problems, has now evolved to let it jump from one profession to another. So, if the branches remain connected to the root… it seems to make a good foundation for building a new language of science, one that doesn’t replace nature with the abstractions of boundless theory.
The paper is a “sampler” of explorations of the topic, including an advanced “starter kit” of methods, terminology and examples, for how to use the patterns of natural design to guide efforts at intentional design and integrate with our world of natural systems. It introduces a way of recognizing natural designs as ‘objects’ in nature, with their own individual boundaries, allowing separate discussion about what goes on inside and outside, and using pattern language (not abstract models) to make verifiable sense of it. Identifying a boundary is what permits considering what goes in and out, and open up the use a traditional use of terms of physics and economics, for understanding the thermodynamics and the coupling between energy budgets and financial budgets, etc. for natural systems. Based on that, it would appear to make a true “object oriented science” a practical possibility.
The original paper introducing this from a traditional biophysical scientific point of view, as “Whole Systems Energy Assessment” (5). That paper can perhaps now be understood if interpreted from a pattern language viewpoint, as showing that shares of GDP measure shares of global impacts of delivering GDP… The economic system does appear to work as a whole, and the effort to validate that seems to successfully result in a far more accurate, and far more actionable, measure the impacts of our choices than efforts to directly trace economic impacts can produce.
For the translation of these and related natural system principles to the language of Alexander’s “pattern language” for defining “object oriented” principles of holistic design see the 2015 “Guiding patterns of naturally occurring design” papers for PURPLSOC (Pursuit of Pattern Language for Societal Change) (Jul 5 2015) (1) and PLoP (Pattern Language of Programming)(Oct 23 2015) (2) and related slides and supplementary materials (3). Also in the directory is a YouTube video link to the first 15 minutes of the slide narration, for the July 5 presentation of ‘Elements’, salvaged from a cell phone recording (4).
Need to update & add notes and discussion on both conferences….
It was reallyexciting to be part of, and to watch this new way of thinking emerge, PL as a whole system language for “designs of services” to balance and support
the traditional view of science as a whole system language for “defined controls“
The traditional scientific method doesn’t fit our new information world very well, with the rapid emergence of so many new forms of knowledge communities, computational science and commerce, seeming to take over. They are also being built on a foundation of science with major problems unsolved, like an understanding of how complex systems emerge and become unstable. The Edge asked What Scientific Idea Is Ready For Retirement?, and got 174 responses, one of which was Melanie Swan’s answer: “The Scientific Method”. She points persuasively to the differences between the emerging computational approaches to knowledge and the traditional practices of science, and hopes a “multiplicity of future science methods can pull us into a new era of enlightenment just as surely as the traditional scientific method pulled us into modernity.”
There’s a flaw in that, though I generally agree with the hope. Science is still unable to study nature except in abstraction, representing nature as a theory of deterministic calculations. It’s been unable to use them to study 1) our own or nature’s great creativity, or 2) any individual thing or event, in its own natural form. It matters because our old habits of multiplying new forms until they caused trouble is now the foundation on which we’re adding an uncontrolled “Cambrian explosion” of new forms of computational (and often disruptive) knowledge. We also appear to be trusting the future of civilization to them, even as the radiation of old forms further depletes and disrupts the natural world. It’s seems we’re “missing something”.
So, my counter proposal is to open the eyes of science to the study individual natural systems as subjects, not just as abstractions, but to learn directly from them, to create an “object oriented science”. My years of work on that, creating a form of physics for studying individual natural systems, works by raising particularly good questions. For example, all natural systems that develop from a common origin as individuals are found to face a common pattern of life challenges, in part:
There are reasons to worry when the foundation for a radiation of new sciences is an “old science” for radiating new forms that make us quite unable to “fit in” on the earth. It makes it likely that the new forms of knowledge instead of correcting that, actually contain the same flaw as the old one. I think a very big part of that comes from science relying on representing nature with equations, that have radically different properties from the subjects that are meant to represent.
A counter proposal…
[first posted to IEET article] Certainly the recent discovery that “the world is complicated” (and both people and nature unusually *inventive*) does expose a deep flaw in the idea that nature follows simple scientific rules and models. That seemed plausible only because some of the simple rules of physics are also so amazingly reliable. Those still exist, and others are to be found most likely, but the question is: “What then do we think of them?”
I think we probably should not throw out the scientific method… particularly just because we’ve been misusing it. The common flaw in our use of science as I see it, and studied since the 1970’s actually, is its “misrepresentation problem”. The world is not a model, and we’ve been treating it that way.
The world is not made of numbers, not made of quantitative relationships. It’s made of organizations of separate things, often found in “improper sets” with the parts of one thing also often taking independent part in others too. It makes things in nature *highly individualistic*, and held together by some kind of “organizational glue” we’ve hardly begun to study. That presents not only a wonderfully interesting “mismatch in VARIETY”, but also several wonderfully interesting “mismatches in KIND” as well. It may not be ‘neat’ but it’s very ‘lifelike’, and opens all sorts of new doors!
So what I think we need to retire is not so much “science” as “the representation of scientific models as nature”. The article points to a number of the big discrepancies that have become too big to ignore, but where does that take us?? One place it takes us back to the age old “million dollar question” of how science is to refer to nature at all. What is it we CAN define that DOES NOT misrepresent what we are studying?? I think a quite simple place to start (and obvious solution once you recover from the shock, I guess) it to treat models not AS nature, but AS “our limits of measurable uncertainty about nature”. Yes, Popper and Bohr with turn in their graves… but models understood as representing upper and lower bounds within which we expect nature to operate, independently, will also be found to be much more useful.
If you actually look closely at natural behaviors you readily see that, that the paths nature takes are always individualized, and we can understand them much better having some information from past events to suggest what to expect. It gives you a straight and clear view of the all-important “discrepancies”. To make use of relieving science of its century (or more) of seriously false thinking, about nature being theory, what you then need are ways for science to refer to nature as “individual phenomena & organizations” to identify the stuff of nature that science studies. In our century or more of trusting abstraction by itself, that’s what I think science has been missing, having a natural object of study.
So, in a fairly direct way I’m calling for an “object oriented science” to correspond to the “object oriented programming” that has become such a big help for giving order to computer coding and the web. My main two tools for that are what I call a “dual paradigm” view (alternating between attention to ‘theory’ and ‘things’), and a “pattern language” view (the emerging scientific method of describing natural organization based on Christopher Alexander’s work).
Alexander’s pattern language is evolving to become a versatile general method for working with ‘recurrent patterns of design’ as ‘whole sets of working relationships’ found in ‘problems’, ‘solutions’ & ‘environments’. My new work describing how these fit together is being presented at the PURPLSOC and PLoP meetings this year, presents a broad picture of the fundamentals, and very worth using to begin the process of recognizing natural design as a working environment. If interested, do searchs for “dual paradigm”, “pattern language” & “Christopher Alexander” both on the web and in this journal.
This is a good introductory description, excerpted from an email, w/ a little edit. The abstract and link are for a paper on “Guiding Patterns of Natural Design:Mining Living Quality” for an upcoming Pattern Language of Programming conference.
Oh, it’s sort of magic..
the hope of course:
is that this emergence of a sound new way to communicate “wholeness in design”
leads to the world ‘transformation to living design’ everyone is so eagerly awaiting…
Pattern language is a new way of communicating design concepts, created by Christopher Alexander, an architect whose ideas came out of the same 60’s/70’s architecture community as mine did, only starting a decade earlier, and he became a wonderful architectural design teacher. Anyway, his idea for how to ‘encode’ principles of ‘wholeness’ for architectural design elements was fairly successful, resulting in a series of books beginning with “A Pattern Language” in 1977, and experiments in urban design as recorded in “A New Theory of Urban Design” 1987, and in attracting a significant following.Then his methodology for defining ‘designpatterns‘ did the magical thing… of being picked up and translated for use in other fields, a real technology transfer, actually representing the encoding of a set of rather ancient and wonderful architectural design principles, for other uses, i.e. “realmagic“! Where it had an amazing impact was on computer programming, becoming the basis of “object oriented design“, as a way of letting programmers communicate and understand their own design objectives, for both the wholes and parts of their programs. Till the late 80’s when this new approach to defining design purposes took hold, programmers really had no good way to define the ‘parts‘ of computer programs, or how they needed to work together to make a ‘whole‘.
So having a way to define “working units of design” seems to me at least to be a big part of why modern programming became so successful, like maybe the other real secret behind the communication power of the internet other than micro-chips. Pattern language lets programmers break computer programs into intelligible workable parts, representing real whole purposes and intentions. It was Alexander’s loving way of describing the pieces of designs that did that, understanding and portraying design as a search for “living quality“. And it caught on. It provides a model for describing
versatile solutions for common problems
as a balance of the forces they resolve
Of course, one of the “forces” is whether we are creating a “living world” or an “inhuman world“, and whether the designs we make can become at home in our environment, to bring us and the earth living quality, or not. That was the issue he was obsessed with from the start. So, like I said, a sign of magic.
What’s more of course, is that his method of defining “design patterns” and my pattern science for understanding “natural systems” are awfully close cousins. You might say they’re much the same thing in several ways, except his focus was on the patterns of wholeness for purposeful design and my focus was on patterns of wholeness in naturally occurring designs. His “search model” for design patterns was “living quality” and mine was for “what makes life lively”, asked as a physicist who happened to have an education in design too. So when I was introduced to his work as it had later matured (I really wasn’t “in the loop” or didn’t “get it” before) and I saw how it was being used by non-architects, I finally recognized the connection and now have lots to do! It’s such a pleasure.