Category Archives: Synapse9 Work & Links

The Growing Rate of Climate Change

Showing:

— A presently elevated growth rate of CO2 in the atmosphere directly linked to globalization.

— And resulting likely 1.5 degree C warming by 2030, TEN years earlier than the recent IPCC estimate.

— Plus a fascinating story of diagnostic data science discovery.

Yes, it is a somewhat radical approach,
but is fully data driven, meticulous, and at the high side of the IPCC uncertainties, making it plausible. So it should challenge others to try to confirm or dispute the findings.
Losing 10 years in preparing for 1.5 degrees C also makes this finding, if true, extremely urgent to respond to.

(A Major Edit of a 10/8/18 version, republished 4/8/19 – Jessie Henshaw)

The Path of Atmospheric CO2 – To understand climate change it helps to start with the whole picture, the great sweep of increasing concentration of CO2 in the atmosphere shown in Figure 1, as the main cause of the greenhouse effect. Looking at where it began, you can clearly see the fairly abrupt shift in the trends at about 1780, also about the same time as rapid industrial growth was beginning, seeming to mark the abrupt emergence of fossil fuel industry that the rest of the curve clearly represents.

Look closely at the relatively lazy shapes of pre 1780 variation in CO2 back to 1500 (purple) and how that pattern differs from the abrupt start of the growing rates of increase (green line) after 1780 an how closely it follows the mathematical average growth rate curve (dotted). Note how the trendline threads through the fluctuations in the data starting from 1780.  The way the data moves back and forth *centered on the constant growth curve* is what implies that the organization of the economy for using fossil fuels had an constant growth rate, of 1.48 %/yr. Hopefully that seems rather remarkable to you, but the data is clear, that the global economy has a single organization for behaving as a whole, as a natural system, with a stable state of self-organization in that period.

Figure 1 – The abrupt emergence of climate change with the industrial economy, evident in the constant compound growth of atmospheric CO2 PPM at 1.48 %/yr, from 1780 to WWII, followed by a pause and then the transition to the even higher growth rate 1.90 %/yr, That second growth spurt, continuing to the present, presumably reflect the modern reorganization of the world economy for maximum growth informally called “globalization.”

We know from the absorption of heat radiation by CO2, creating the greenhouse effect, that the CO2 greenhouse effect is heating the earth in relation to its concentration in the atmosphere. What implies that relation is close to linear, making the effect directly proportional to the cause, is shown in the Figure 2. The dashed brown line shows the slope of the relation, closely fitting the actual gradual curve, at least between 300 to 400 PPM, the thresholds that were crossed in 1914 and 2016 respectively, a period of 102 years. Atmospheric CO2 is increasing much faster now, though, so the next increase of 100 PPM, to 500 PPM, will be reached much more quickly rising at its current stable rate of 1.9 %/yr rate. If that rate continues 500 PPM will be reached in only 30 more years, by 2046. That large acceleration is the effect of the current higher exponential rate of increase. Of course, considering the rapid compound acceleration of the cause of climate change, and the alarm people are taking now, quite a lot could happen before 2046.

Figure 2 – The “relative heating” of the the earth to Atmospheric CO2 concentration, indicating temperature change has an approximately linear relation to CO2 (brown line) for the range of concentrations (300 to 400 PPM) over recent times. The triangles indicate concentrations in 1985. (Mitchell 1989, Figure 6 w/ added color)

The Annual PPM Growth Rates – Figure 3 shows the growth of Atmospheric CO2 (green) with the details of its fluctuating annual growth rates, to depict both the constants of the growth curve and it’s irregular growth rate interruptions. The individual interruptions raise lots of interesting questions, but perhaps the most important feature is that they are quiet temporary, as evidence of the constant behavior recovering again and again.

The upper curve shows fluctuating annual growth rates (lt. axis, PPM dy/Y) for the curve below, the CO2 PPM concentrations. The peaks and drops of the growth rate align with the small waves in the concentration (rt. axis). Note that the large drops in the growth rate that seem to snap right back to the the horizontal dashed red lines. That seems to show that they mark processes that absorb and then release CO2 again, as they do not seem to affect the average growth rates of PPM concentrations as a whole, around which the annual fluctuations homeostatically fluctuate.

This diagnostic approach is for raising questions like the above, using the annual growth rate to expose the dynamics of the curve for a somewhat anatomical picture. In this case it’s of the homing dynamics of the global growth system as it first hovers around the rate 1.48 %/yr from 1780 up to WWII, and then shifts to hovering around the higher rate of 1.9 %/yr as it stabilizes from 1971 to 2018. You might think of these two long periods of homeostatic growth rates in CO2 concentration as representing periods of regularity in the causal systems, global economic growth and the carbon cycle response, seen through the lens of atmospheric CO2.

You might think the large departures from the regular trends would be great recessions perhaps, that then “make up for lost time” on recovery. I could not find corresponding recessions, though, and for the great recessions I checked there do not seem to be notable dips in CO2 accumulation. To validate this kind of research one has to go through that kind of thought process for every bump on the curve, either a tedious or exciting hunt for plausible causes than then check out with other data.

What seems most unusual about the big dips in the CO2 growth rate (D1, 2, 3, 4, 5) is that 1) they do not occur after WWII and 2) they rise and fall so sharply and have no lasting effect, seemingly temperature sensitive as well as absorbing CO2 later released. I can’t say whether it is feasible or not, but something like vast ocean plankton blooms might have that effect, absorbing and then releasing large amounts of CO2. There’s also a chance the way the raw data was splined and the growth rates smoothed, to turn irregularly spaced measures into smooth curves, might also have unexpected effects. Whatever phenomenon causing the big dips was, it appears to have been interrupted by the rapid acceleration of warming that followed WWII, as evident in the smooth and uninterrupted rise in most recent and best raw data. Those are at least pieces of the puzzle that might help someone else narrow it down.

Figure 3. Atmospheric CO2 concentration (CO2 PPM)(rt. scale) and its annual growth rates (PPM %/yr)(lt. scale), showing the change in growth constants before and after WWII. The key evidence of these being different organizational states of the world economy (before & after WWII) is regular “homeostatic” (home seeking) reversal of trends departing from the growth constants. It is the post WWII growth constant state of 1.90 %/yr that is preventing normal policy process from intervening in climate change, and needs to be “recentered” on learning from nature rather than overwhelming nature for our survival. 

Comparing the CO2 cause and degree C effect – The main purpose of Figure 4 is to compare the history of earth temperatures (blue, ‘C, lt scale) with the curve of atmospheric CO2 (green, PPM, rt scale). The CO2 PPM data is the same Scripps atmospheric CO2 data and scale we’ve seen below. The temperature data is from the HadCRUT4 records used by the IPCC. In this case the original anomaly data relative to the 1850-1900 average have been converted to absolute ‘C values, using a conditional set point of 14.6 ‘C in 2017. In a way it is as arbitrary a coordinating value as the others people use. It’s chosen here first for being a more familiar scale, but also so that 1780 initial values for PPM and ‘C can be determined as initial values for the greenhouse effect. Those baselines are essential for defining the exponential growth rates of the PPM and ‘C curves. The 14.6 ‘C value was based on an expert’s estimate.

Figure 4

Aligning the curves for Figure 4 lets us look closely to see if any shapes of the cause of the greenhouse effect (PPM) are clearly visible in the shape of the effect, global warming (‘C). Does anything in particular jump out? First might be the differences, one curve quite smooth the other jittery, both having wavy fluctuation patterns too, but of very different scales and periods. The first thing you might ask about is how regularly irregular the ‘C curve is seems to be.  That variation is thought to be mostly due to annually shifting ocean currents, along with weather system changes and the difficulty of measuring the temperature of a complex varying world.  

The ‘C curve (Figure 4) also shows the two ‘Great waves’ (#1 and #2) in earth temperature that appear to be independent of the greenhouse effect. The dotted red line was visually interpolated as the midline of the irregular but seemingly quite constant fluctuating annual temperatures of the HadCRUT4 data. The blue dotted line was added to suggest earlier large waves in earth temperature copied from the shapes in the ancient temperature reconstructions seen in Figure 5. I physically overlaid those reconstructions of ancient temperatures on Figure 4, drawing a continuation of the Figure 4 midline curve that fit the Figure 5 curves.

One might say the minima of the great waves in the ‘C curve display a trend somewhat like the general trend of the PPM curve, say from 1780 to 1980. The one shape that makes the two curves seem really connected, though, is the way the sharply rising PPM curve (the implied cause) and ‘C curve (the implied response) both start following a “hockey stick shape” in the 1980s. It even seems the shape of the ‘C curve interrupts the great waves as it takes off exponentially, breaking a rhythm that seems to go back many centuries. There is a possibility that the great waves represent upper atmosphere standing convection patterns waxing and waning, something that increasing convection intensity could interrupt. Perhaps that would help others find what the great wave cycle, or not. Since theory suggests the trends of both cause and effect have a linear component Figure 6 shows a linear scaling of the PPM curve to see if it and the ‘C curve can fit.

Figure 5 – NOAA (2007) 1300 to 2007 Northern Hemisphere record of temperature reconstructions. Measured from a 1881-1980 baseline. This it taken from a longer history keeping the units and adding a title and dates 1780 and 1880 (brown). That is the period after the greenhouse effect began before it was visible in the records of earth temperature. The red line shows an old NOAA speculation that warming developed earlier and slower than found here.

Scaling CO2 PPM to Make a ‘C Proxy – The reason to scale PPM to emulate the dynamics of ‘C curve is simple. The ‘C fluctuation is so erratic the variety of curves to predict its future is rather extreme, so people have been generally using a straight line. An exponential curve is not a straight line, though. So the quite regular shapes of the PPM curve, including its clearly measurable growth constants, 1.48 % before and 1.9% currently, do make it a prime candidate as a useful proxy. Even if the trend has a clear direction now we of course have to allow for increasing uncertainty over time. Adding to that are the plans for dramatically cutting CO2 despite a world economy dramatically increasing its production, a tug of war that could be interrupted by actual war or other economic downturn.

Where the current stable growth rate of climate change seems headed, knowing the PPM curve should be linearly proportional to the greenhouse effect, we experimentally scale CO2 PPM see if it fits the ‘C curve in a logical way (Figure 6).

Scaling the PPM curve to fit the ‘C curve makes a PPM’C proxy curve, hoping to fit the midline of the highly irregular ‘C curve from 1980 to the present. Both the units and the baseline are not determined, though, to produce the proxy curve in PPM’C = A*PPM + B, using a linear scale factor A and a baseline B. A third determinant is then finding a optimal fit between the very different earlier shapes of the curves. So basically I tried lots of things, and found my initial assumptions were mostly wrong. Initially I made the mistake of trying to fit the PPM’C curve to the midline of the earlier ‘Great waves’, and tried several ways until it was clear they were all wrong.

Then I realized those earlier great waves were really not related to the greenhouse effect. So my greenhouse effect projection might better be interpreted as coming up under the earlier systems, like it actually looks. That was purely a graphic device at first. Then when I adjusted the PPM’C curve to pass under the ‘Great Waves’ I set it to go through the miline of local fluctuations instead of the Great Wave departures. Suddenly the fit of rapid growth period became as perfect as I could ask for. I spent some time trying to figure out why, studying all the loose ends, in the end resolving that’s what the data seemed to say. That PPM’C curve then becomes the hypothesized most likely “real” rate of greenhouse effect climate change, and offering a much more narrowly regulated way for projecting its future. 

Figure 5 shows both the best fit scaling of the PPM’C proxy curve (dark green dashed line), and its extension to 2050 at its presently stable growth rate of 1.90 %/yr (dashed light green line). Yes there are various uncertainties, but the threat of climate change so far has seemed to be from underestimating, not overestimating, and the findings do appear to be well within the IPCC uncertainties given the difficulty of projecting the temperature data directly.

I think it means that reaching 1.5 ‘C by 2030 is a much more probable estimate of the current trend than reaching 1.5 ‘C by 2040.

Figure 6 – The PPM’C curve scaled to closely fit the HadCRUT4 data and then projected at the homeostatically stabilized growth rate of observed in atmospheric CO2. How long this projection might hold depends on how robust the global natural and economic systems driving the growth rate in atmospheric CO2.

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The Economy as a Whole – How great a new threat this acceleration in atmospheric CO2 pollution and its greenhouse effect are seems to rest on just how stubborn the global homeostatic regulating systems observed are. That could really change the climate mitigation picture, and help explain why there has been only negative progress in slowing CO2 pollution. So far is seems to have been neglected, with negotiation over mitigating climate change not seeming to take into account the organizational inertia and persistence of the global economic system as a whole.

Figure 7 shows a group of major indicators of the global economy that were selected for having constant growth rates from 1971 (the earliest data for some) to 2016. The GDP PPP curve in trillions of 2016 dollars is growing the fastest, and each of the other curves was indexed to GDP in 1971 in proportion to their relative growth rates. For example, since total economic energy use is growing at about 2/3 the rate of world GDP that variable was scaled to 2/3 of GDP at 1971. This device displays the steady relation between them called “coupling.” That the same proportionality of the growth curves is constant throughout it indicates each of these curves reflects the behavior of the same system. What seems to cement the view that the global economic system appears to be behaving as a whole is the visual evidence that the data of each of these series, like the CO2 PPM data we discussed at length before, seems to fluctuate homeostatically about the growth constant.

What physically coordinates the economy’s coordinated relationships between different sectors displayed here as growth constants seems likely to be cultural constants of each cultural institution, or “silo” of the world economic culture. Every community seems to develop its own expected way for things to work and change and seems to become the way the different sectors end up coordinating their ways of working with each other. That all of this is organized primarily around the use of the exceptionally versatile resource of fossil fuels then indicates that a deeper reorganization of the economy than a swapping of one set of technology for another will be involved. It should suggest to any reader just how very much of the world economy would need to be reorganized, and to be reminded that the last times the world economy was sufficiently disrupted to be reorganized were during WWII an the 1930s.

This topic is also the subject of a longer research paper. Science review drafts are likely to be available later in April 2019.

Figure 7. The global economy working remarkably smoothly as a whole system of coordinated parts, seemingly much like theory says it should, but most people don’t see because they don’t look at the behavior of the system as a whole.
Figure 8 – Smoothed annual growth rates of recent world energy use and CO2 emissions, showing close coupling of their fluctuations with relatively insignificant trend.
Figure 9 – Log Plot of Figure 7 variables with a 1780 to 2020 time scale. The backcasting of their exponential constants displays the convergence with the backcast GDP trend of four of them at ~1935 and with two others (blue circles) at ~1920. The effect implies the stable coordination of the parts of the global economic growth system established by the 1970’s was in the 1920s and 30s.

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A draft paper Coupling of Growth Constants and Climate Change has full details on the data sources, methods and references:

Data Sources:

  1. Atmospheric CO2 PPM 1501-2015: OurWorldInData.org 
    https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions 
    as well as from the Scripps source directly:
    (Scripps, 1958 to present)(Macfarling Meur 2006)  
    http://scrippsco2.ucsd.edu/data/atmospheric_co2/icecore_merged_products 
    “Record based on ice core data before 1958, and yearly averages of direct observations from Mauna Loa and the South Pole after and including 1958.”
  2. HadCRUT4 earth temperatures 1850-2017 – Rosner:   https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions
  3. The world bank was my source for GDP PPP and for from 1990 to 2016
    – https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.CD?end=2016&start=1990
  4. World Food Production – 1961-2016 FAO:
    http://www.fao.org/faostat/en/#data/QI
  5. World Meat Production – 1961-2016 Rosner – OurWorldInData: https://ourworldindata.org/meat-and-seafood-production-consumption
  6. Modern CO2 Emissions – 1971-2016, Archived IEA CO2 data extended with WRI CO2 emissions: https://www.wri.org/resources/data-sets/cait-historical-emissions-data-countries-us-states-unfccc
    – Because the latest economic CO2 emissions data is 2014 not 2016 as for other data, the trend of atmospheric CO2 was used to project the economic emissions data for the last two missing data points, showing no anomalous direction. https://www.co2.earth/annual-co2
  7. BP offered energy data in MtOe in its “Statistical Review of World Energy – all data 1965-2017”
    – https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy/downloads.html
  8. The IEA news item statement that CO2 flattened for 2016 and 2017 was used, just to show how little effect it would have if true
    – https://www.iea.org/newsroom/energysnapshots/global-carbon-dioxide-emissions-1980-2016.html

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Work in progress…
Below this line is old text that may be edited in pending updates.

It’s a powerful technique for understanding complex systems, such as the world economy, that behave smoothly as a whole.   The most important observation is just that.  The system as a whole and these whole system indicators are not separate variables, and the smoothness of the curves shows the system as a whole behaving smoothly as a whole over time. 

From our local views of the world that often does not seem to be at issue, though it really is the main force behind all the changes everyone is struggling to adapt to.  Individual businesses, cities and countries generally have a quite irregular experience, as their roles in the whole continually change.  What the smoothness of the curves and the change in the system as a whole really means is that the world economy is working the just the way it is (financially) supposed to.   It is being globally competitive the way money managers manage it, and continually reallocating resources and business to where they will be best utilized, resulting in most every part having somewhat irregular experience to make the whole behave smoothly.   The uniformity of these global indicators also says is that their origins all point back to ~1780, when modern economic growth began.   We have reasonable measures US economic growth from ~1790, …and so went the world!  

Smooth exponential curves and the systems generating them are, of course, among the things of nature with inherent “shelf lives”, relying on systems of developing organization of multiplying scale and complexity, certain to cross thresholds of transformative change.   In nature, growth systems generally develop to one of two kinds of transformation, stabilization or destabilization, the crashing of a wave that doesn’t last for example or the thriving business that can last for generations.  What characterizes the difference for the emerging systems that last is that, while becoming strong with compound growth (like the systems that don’t last also do), they become responsive and refine their systems to stay strong.   In economic terms that’s remaining profit seeking they “internalize their externalities” to mature toward a peak of vitality rather then failure.   It’s a choice made in mid-stream.

Understanding what will make that difference in outcome for our global growth system will partly come from people getting a better understanding of how we got here, as shown in the Figures 1, 2, 3 & 4.   The growth of technological civilization relies on ambition, creativity and resources, and methods that we could potentially change.  How economic growth is largely managed by the application of business profits to multiplying business developments, what makes GDP to grow.   If our decisions were to internalize our externalities that is also one of the things that might change, without really changing human ambitions, creativity or resources.

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JLH

NYC data Science… examples

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”.

 

1. The collapse of the crack epidemic in NYC began in 1990,

… 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

A pitch for introducing bigdata “system recognition”

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.

empirical evidence of systemization

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.

Thanks for listening!    –     Jessie Henshaw

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fyi – 350 words Continue reading A pitch for introducing bigdata “system recognition”

So What’s Pattern Language? Mining design patterns from nature.

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.  

Alexander’s 15 Principle Elements of Wholeness – adaptation by TKWA archt.

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 ‘design patterns‘ 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. “real magic“!  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

  1. versatile solutions for common problems
  2. 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.

The work is to begin translating between the pattern language I developed for myself for naturally occurring designs, and the one that is becoming the common approach of many professions and communities based on Alexander’s approach, and do what I can to contribute to adding to the versatility of that common approach. Continue reading So What’s Pattern Language? Mining design patterns from nature.

Natural Pattern Languages

key organizational elements for the working relationships of complex systems
ideas of complex relationships that fit the reality

We care because of the new bridge it creates between human ideas and the working organization of complex working systems we make, use and need to respond to of all kinds, an emerging broad advance in understanding complex system organization design.  The idea of pattern language, invented by Christopher Alexander for architectural design in the 70’s, actually started blossoming some time ago, it a most surprising place, in the creation of complex design concepts for computer programming known as “object oriented design”.

As it continues to expand and mature it is becoming a wonderfully versatile method for sharing and recording expert understandings of “how relationships work”, with application to almost any fields.   It became the basis of modern computer programming, as “object oriented design“, with each object fulfilling a “pattern of relationships” that connects with others.   For me… its a language I can begin to use to translate my research on natural system designs into, into “JPL” (aka Jessie’s Pattern Language), for subjects such as how natural systems transition from “type-r” to “type-K” behaviors (a subject underlying much of the discussion on RNS of complex system successions,life stages and cycles,”dual paradigm views”, “organizational stage models”, as observable patterns of organized change in relationships).

The reason it works for “object oriented” programming and “natural systems science” and in other areas too, appear to be the same.   Pattern languages let people use their considerable natural understanding of complex relationships, like “home” “friends” “communication” “trust” “patience” etc. to open our eyes to similarly complex working relationships and meanings of complex systems elsewhere too, as “designs”.  The standard “design pattern” of pattern languages connects human relationship concepts to working organizational relationships of behavioral systems  of ANY kind.  That seems to be why the design model that Alexander invented turns out to be so adaptable to our needs in our now overwhelmingly complex new world…!   ;-)   I can see it readily becoming applied to breaking down the silos of separation between knowledge disciplines, too, the so called “blind men and the elephant problem”, something just completely unimaginable in reality today.

Pattern Languages are for

1. identifying key organizational elements in systems of complex relationships, found in nature or in design practice,

2. communicating design elements for complexly organized systems or illuminating them in existing natural or manmade ones.

3. using the design pattern to refer back to the original natural forms and contexts from which it originated or is used to represent.

Two natural system design patterns, (for example):

Moving with the Flow

Sometimes you watch the people, sometimes their flows.   The flows are roles in larger scale systems of group motion, forming as people avoid interference, but can confine them till they find an opening too.   Markets flows form paths and break from them as new paths are found, often flocking in chase of a wave of anticipation, or uncertainty moving leaderless floods.   Those are puzzling, since there may be no news the contagious change in direction, but systemic change generally usually has a real cause.    Flocks of birds appear to do it just for fun though.

 

 

Alternating roles that Fit

Both natural and human designed complex  organizations have independent parts that create emergent properties by fitting multiple roles.  Day and night, male and female, work and relaxation, pencil and paper, cup and liquid, all the amazing polarities that produce reliable results because of how they fit their multiple roles, quite unlike any set of fixed rules could ever do.  The trick is only physical parts and their relationships can do that, and a pattern language those relationships provide a way to develop concepts for understanding the working parts.

 

 

There are many types of Natural Pattern Languages, generally depending on the organizational medium (material and environment)

  • Social organization pattern languages
  • Natural system pattern languages
  • Architectural and Urban design pattern languages
  • Cultural pattern languages
  • Abstract Scientific pattern languages
  • Educational pattern languages
  • Computer knowledge design pattern languages
  • Commons & community design pattern languages
  • Economic pattern languages
  • Movie making pattern languages
  • Organizing pattern languages
  • … etc.

 

There are three uses of the term “pattern language”,

1. As the collection of design elements and patterns used to design or describe working complex systems

2. As an the organizational language of an individual design project describing its working relationships as a whole

3. As a property of an individual complex system, consisting of the working relationships between its parts and its environment, that might be view from various perspectives to recognize different elements.

 So they’re simple conceptual models designed as versatile tools for engaging our minds with the actual working organization and relationships of natural and designed complexly organized parts of our world. So they come in those two basic forms, as Design Patterns one uses to guide the implementation of some plan or as Natural Patterns used to help people understand how designs can fit in with natural organizations.

 

Pattern Language sites

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jlh

But how can physics study behaviors, not the theory?

On @SFIScience David Pines, Co-Founder of the Santa Fe Research Institute wrote Emergence: A unifying theme for 21st century science, describing a critical need for physics to develop a way to study “emergence” directly, as a natural phenomenon, not just a theoretical models.  This article reposts my reply to him on Medium: But how can physics study behaviors, not the theory?

For understanding the emergence of new forms of organization in nature, the study of theoretical models seems not to be yielding the kind of useful understanding we so critically need now.   What I introduce is a”dual paradigm view”, to address the dilemma, a better technique for learning from nature directly.  Computer models are fine for testing theory, but need to be used differently to help us follow the continuities of nature.   There is a very big conceptual hurdle, getting mathematicians to study the patterns of nature directly…   The physics based method I developed, using models of probability to help locate individual developmental continuities offers a direct way to address the problem Pines raises.  It could genuinely offer complexity science a better way to study their actual subject, and couple their theories to actively occurring emergent processes and events. Among other discussions of it on RNS Journal:

a”Dual paradigm view” Can ecosystems be stable?,
 Finding Organization in Natural Systems – “Quick Start”
– 
Can science learn to read “pattern language”…?
 In two words… what defines “science”?

– ‘Big Data’ and the right to human understanding.
– What is a “rights agenda”, with ever increasing inequity?
 Sustainability = growing profit then steady profit

Emergence is what we see from cosmic events to the flocking of birds…

 

David Pines makes a very intelligent assessment, saying in part “The central task of theoretical physics in our time is no longer to write down the ultimate equations, but rather to catalogue and understand emergent behavior in its many guises, including potentially life itself.”

I was one of those who figured out why that would become necessary back in the 1970’s. The behavior of complex systems of equations that permit true emergence will not be knowable from the equations. It’s not just their complexity, but that their emergent properties are emergent and dependent of histories of development rather than being formulaic.

I have also been writing papers and corresponding on the problem very widely since then, and really wondering why I was so unable to get systems thinkers, from any established research community to join me, in studying the commonalities of individual emergent systems. I started with air currents, that generally develop quite complex organization quickly with no apparent organizational input, behave very surprisingly, and seem individually unique.

I actually developed a fairly efficient scheme for studying any kind or scale of emergent system, using the simple device of starting with the question: “How did it begin”. What starting with that question does is immediately shift the focus of interest to considering systems as “energy events”, that you consider as a whole in looking for how they developed. That approach also directs you to look for the event’s naturally defined spatial and duration boundaries, which are highly useful too.

In addition to being fairly productive as research approach, it also made it easy to skirt lots of spurious questions, like “how to define the system”. With that approach your task is finding how the subject defines itself, still looking for a pattern language of structural and design elements to work with, within and around the system, confirming what you think you find.

What I finally arrived at in the 90’s was that the equations of energy conservation implied a series of special requirements as natural bounds for any emerging use of energy. I was thinking that the issue was how nature uses discontinuous parts to design continuous uses of energy, and in working with the equations noticed that the notation for the conservation laws were either integrals or derivatives of each other.

Then one afternoon I just extrapolated an infinite series of conservation laws to define a general law of continuity, and integrated it to find the polynomial expansion describing the boundary conditions for any energy use to begin. It was a regular non-convergent expression, a surprising confirmation of Robert Rosen’s interest in non-converging expressions for describing life, and became very useful as what to look for in locating emergent processes to understand how they worked. I circulated the proof for discussion many times, submitted it for publication a few times and wrote numerous introductions, the following the most recent:

Continue reading But how can physics study behaviors, not the theory?

Review of Science for UN’s SDG’s

A brief report and links to presentations for the Mar 20, 21 Science meeting at the UN

 

There was an Expert Group Meeting on Science and Sustainable Development Goals at the UN last Wed & Thurs.  Most of the presentations would be easy to get the sense of just from the slides.   I think worth the trouble.    Below are the links and very brief notes on my impressions.   There’s also background information on science and the Rio & Agenda 21 issues: Science; Sustainable development goals;

{j} A brief report. The 10 presentations are highly informative. One in particular raises grave concerns.   The apparent dominant view in the sustainability sciences from #1 still seems to be that “decoupling” is a realistic objective, if we just “innovate”.

If asking the hard questions suggested by #9 we’d acknowledge “decoupling” is an idea to have ever growing wealth and ever shrinking resource needs as our future plan.  We’d also ask whether resource limits are what matters in the end, or whether ever larger and faster change in how we live would become unmanageable anyway…

There also seems to be no direct measure to use for determining if SD goals are achievable or sustainable.  My presentation, if I were to make one, would offer the science to fill that gap.

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1. Decoupling – Natural resource use and environmental impacts from economic growth – Mr. Fischer-Kowalski & Mr. Swilling, International Resource Panel and UNEP

{j}  – The fervent dream in some quarters that we might create ever increasing wealth without resources (“decoupling”) is still at odds with the long established and continuing trends.  It seems presented here as still a hopeful challenge rather than something probably dangerous to rely on.
– see also Apr30 2014 “Decoupling Puzzle – a partial answer

2. Early warning of climate tipping points – Mr. Tim Lenton, University of Exeter

{j}  – It would be great to hear the full presentation, as the new information I see right at the top of this is quite shocking, that the climate change expected not too far off, is a relatively abrupt shift from one stability range to quite another.

3. From MDGs to SDGs: Key challenges and opportunities – Mr. Dave Griggs, Director, Monash Sustainability Institute, Future Earth

{j}   – several nice conceptual diagrams, hopes and fears

4. Future Earth: research for global sustainability – Mr. Stephen Zebiak, Earth Institute, Columbia University

{j}   – presents a world science collaborative called “Future Earth”, to guide all parties in making decisions from a scientific basis, so, a ‘multi-stakeholder’ process for science to speak, that I think would succeed and fail as the IPCC did unless it includes the financial and business communities, AND, the three of them use real measures to determine what profitable scenarios are actually sustainable.

5. Strengthening the science-policy interface – Global Sustainable Development ReportUnited Nations Department of Economic and Social Affairs (UNDESA) Mr. Richard Alexander Roehrl, Division for Sustainable Development

{j}   Nice presentation on trends in research, and list of upcoming assessments of progress and potential,  but talk of global modeling of system change as the reference indicator,

6. Strong support for SDGs from the scientific communityInternational Council for Science (ICSU) Mr. Gisbert Glaser, Senior Advisor

{j}j   Science strongly supports the funding of the great scientific research being done

7. Sustainability is political – Building pathways in a safe and just space for humanity – Ms. Melissa Leach, Steps Centre

{j}   Good selling points for succeeding, but not clearly connected with paths to success as I see the main dilemma and barrier to selling it

8. The role of science and scenario modeling in setting priorities for SDGs – Ms. Claudia Ringler, International Food Policy Research Institute

{j}   Focusing on hunger, the benefits of succeeding and the costs of not

9. The role of science and scenario modeling in setting priorities for SDGs? – Youba Sokona

{j}   A planning exercise, last three slides ask the hard questions…

10. The role of science and scenario modeling in setting SDG prioritiesUnited Nations Department of Economic and Social Affairs (UNDESA) Mr. Mark Howells, Royal Institute of Technology (Kungliga Tekniska Hogskolan)

{j}   Nice display of the complex system modeling approach contemplated, that misses the financial need for compound returns for financial system stability, and so for the system to accelerate outputs to infinity…

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Jessie Henshaw 3/23/13

Old Blog Site changeing

I’ll be switching from posting at “synapse9.com/blog” to “synapse9.com/signals” where I’ve installed a more functional blog. Nothing much fancy happening, but upgrading the tools of my original blog to WordPress 3.1.3.

You’ll still find this collection of posts covering the past 6 years of comment and research notes on the new science for a natural world here, until I figure out how to transfer them. Please do come there and subscribe to the RSS feed to keep following my new work and explorations of the subject.

My research archive, The physics of happening, is still at my domain along with my collections of images, reference libraries, introductions and writing www.synapse9.com.

The old blog site just got to be a problem, unable to archive the posts except by printing them to PDF’s (!), unable to separate the spam from comments, no text formatting options, etc. …It was kind of primitive.

My subjects and writing style, of course, will remain just as “primitive” as before…(whether you saw that as a liability or benefit I leave to you), so the software upgrade won’t really change anything but the look and feel of the pages. ;-)

 

Recent additions to Concept and Comment list

List of short articles on main website: Concept and Comment.  Here are some recent additions – Happy Thanksgiving 2009

Peak Zucchini – 11/20/09 The story of overabundance and when to give it away for our Thanksgiving…
What in the world is really going on here? – 11/15/09 How our work ethic accidently pushes us up an ever steeper learning curve
How we get out of this – 11/11/09 What to do when it’s your solutions that become the main cause of your problems
Inside Efficiency – 11/10/09 The mystery of why doing tasks ever more efficiently multiplies their services and impact growth
The Missing Variables in Thermodynamics – 10/24/09 The unhidden but missing energy that builds energy flow systems
Lines of Sustainability – research notes on defining the limits of sustainability and points of vanishing returns.
Economies That Become Part of Nature – how we can, why we have to
When = becomes a sign of change – The real “millennium bug” is all the = signs becoming ? marks.
Efficiency Mistake – the main unwanted reverse effects of efficiency & productivity 5/27/09