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The Growing Rate of Climate Change

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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

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Evidence of Decoupling Still Zero.

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The Growing Effort to Decouple GDP from Energy use and CO2,
is having no apparent effect,
raising serious questions about the nature of our plan.

The graph below (Figure 1) shows the 46-year record of world GDP PPP, Energy, and CO2, during which their growth rates have been in constant proportion to each other, called their “coupling.” The things to read are 1) the lack of accumulative departure from the steady trends, and 2) how closely the exponential trend lines (dotted) follow the data.

It shows that the long trend still holds despite both big efforts and bigger promises that accelerating growth using more efficient processes would separate the expanding economy from its impacts.  Focusing so much on the “positive” completely disguised the big picture, though, that in 46 years there has been no accumulative effect at all.  So there’s a lot to explain, yes, but the graphs below show persistent regular behaviors of the economy as a whole resilient system, a problem not yet faced at all.

Figure 1. Coupled Growth Trends of World GDP, Energy & CO2, showing how the three move together at proportional growth rates, as parts of a whole system.

That energy use and CO2 emissions are now still growing at the same rate as 40 years ago is strong evidence that none of the sustainability measures such as exceptional efficiency gains said to decouple the economy from its impacts, have had any effect at all.

The irregularly fluctuating curves below (Figure 2) show the annual rates of coupling if world Energy and CO2 growth rates to World GDP (PPP).  The scale at the left shows their locally averaged growth rates as a fraction of the locally averaged GDP growth rates (to somewhat smooth the curves) going below zero once.  The important thing is to notice is that the fluctuations vary around nearly horizontal trendlines.

It’s as if the economy is guided by an “invisible hand” keeping the fluctuations symmetric to the near constant trend.  It says the fluctuations have been adding up to no effect.  The likely cause of this is how a competitive economy naturally works.  Technology and resources are supposed to be treated as being fungible assets, to be constantly reallocated to maximize profits.  In the data, that functional coupling between the physical and financial systems of the economy is shown working rather smoothly, replacing less with more profitable assets to maximize the growth of profits for the whole.  That stable coupling of managed assets to growth is then an apparent natural emergent property of the system as a whole, as a partnership between human cultures and the financial world’s effort to maximize growing profits.

Figure 2. Regular fluctuations of Energy and CO2 coupling with GDP, have repeatedly been claimed to be evidence of rapid decoupling… ignoring how very regularly the periods of apparent decline were followed promptly by reversals, as if irregular waves of water seeking the average level. 

 How the world community came to say that “sustainable development” would reverse this stable natural relationship between the economy and its resource uses is described in more detail in April 2014 in The Decoupling Puzzle. Small fluctuations do keep causing excitement for both devoted climate deniers and sustainability advocates, though, each picking out brief trends seeming to affirm their hopes, like the five periods of apparent rapid decline in CO2 to GDP coupling shown here.  The real evidence is that the local fluctuations never seem to result in a change in the direction of the whole, like ripples on a pond that always level out.  The latest dip in the CO2 coupling trend has been claimed as a sign of turning the corner by the IEA, clearly unaware of the consistent pattern of that metric repeatedly fluctuating around a near-zero trend.

Added perspective on the global data is gained by plotting the ratio of GDP to Economic Energy energy, the amount of wealth produced with a unit of energy.  We call that variable “Economic Energy Efficiency,” the amount of economic wealth generated per unit of energy.  Having its growth rate = 1/3 the GDP rate implying that improving efficiency contributes 1/3 of the value of energy to the world economy, growing Energy use contributing 2/3 if the value.  That ratio demonstrates a general case of Jevons famous observation that in a growth economy efficiency results in growing rather than declining resource use and impacts.  Any way one reasons it, what is crystal clear is that in the last 46 years strenuous effort to use efficiency for sustainability have had the opposite of the intended effect, recreating the original problem rather than solving it.

Figure 3 The share of GDP growth contributed by Economic Energy Efficiency proves Jevons principle that in a growth economy efficiency multiplies energy use and all its accumulative earth impacts.


So we need to be suspicious of the world policy to maximize growth at any cost.  The costs are rapidly swelling not shrinking.  The other coupled impacts of growth also causing how people live being forced to change ever faster creating major disruptions and dissension all over the world is one of the biggest, though even the NGOs are very slow in recognizing.  In nature, growth is how all kinds of natural systems begin, but those we admire for their perfection turn to refining their designs before they climax rather than, driving their growth to the point of being torn apart of being exhausted.

That’s the trick.  Maximizing growth might seem logical as a way for societies to keep up with social distress and debts, but now it’s accelerating them.  So now we need to balance the attraction of short-term profits and connect them all the unbalanced disruptive changes that now surround us.  We talk lightly about replacing people with robots, for example, overlooking that the robots only work for the banks.  That’ll make people and governments ever more indebted and incapable of responding to climate change, for one problem.  And that chain of consequences goes on and on, that is as long as we keep ignoring how natural growth systems that avoid the problem work.  More disruption is not the solution, only moderation.

There’s an alternative business model that could serve as a general design for growth without disruption, one that switches to paying the profits forward once any debts have been paid back.  Once understood, that is what would achieve truly integrated, thriving and self-limiting development, as biomimicry of ecosystem designs.  It is discussed in more detail in the article linked from my next post, Culture, Finance-for-Development, and tPPPs.

Use biomimicry for how nature uses growth to build thriving and enduring systems.

It would be a way for businesses large or small to begin to experiment with how nature succeeds in creating beautiful, thriving, and purposeful systems.  It’s a fairly simple formula.  It’s also a practice we all know well for how to successfully relate to other people and how to successfully complete business or home projects.  It starts with building up innovations to then select what to refine for making the result resilient and purposeful in its environment.  If we approached every new relation or project by piling on new experiments with no turn toward refining something to last in the end, all the effort would go to waste in the end.

To start you study the similarity between nature’s way of building things to perfection and how we do our own home or office projects! They all take place in “three acts.” The first act is for “innovating,“ the second for “refining,“ and the third act the “release” of the finished product into its waiting environment (IRR).  You see the same three acts in the birth cycle, and in the start-up of new businesses too, as well as the formation of new cultures and most every other kind of individual development.  The trick is really to pay attention to the inspiration that starts it off, as something to fulfill.  That lets you anticipate and move smoothly between the stages of emerging development, first adding up more innovations, then refining the ones worth keeping to the end.  It’s what comes most naturally when we can see the whole effect.

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When you can see the whole it’s easy to recognize the point when adding more innovations begins to work against getting something finished, called a “point of diminishing marginal returns.” Of course on a home or office project what tells you it’s time to shift to finishing what you started is just sensing what can you finish while you have time and resources.  For anything measurable, like wealth, the point of diminishing marginal returns is when it becomes more profitable to put efforts into getting things to market rather than try more experiments.  To apply it to the world all you do is ask: “What is our real plan here?” and look around for how to perfect what we started, and at the right time stop taking on more and more that we probably won’t be able to finish.  It’s a matter of shifting to pursuing achievable goals rather than hanging on to thinking ever bigger with no end in sight.  Reaching for the right goal doesn’t necessarily make the work easy, of course, particularly for big personal, community or business projects.  It just makes the work a lot better, and the end something fulfilling and rewarding.

I discuss that as a way to measure truly lasting success for the UN’s 2030 Agenda and its Sustainable Development Goals, instead of just “more, faster” the ways the UN’s goals are like the goals of business-as-usual, discussed in more detail in Culture, Finance-for-Development and PPPs.

JLH

Analysis Notes:

  • The global GDP PPP curves show IEA data from 1971 to 2008 spliced to overlapping World Bank Data from 1990 to 2016.  The curves for global Energy are from BP statistics, and the Global CO2 curves show data from WRI.
  • The Energy and CO2 curves were each scaled to the GDP curves in proportion to their average growth rates for a graphically clear and honest comparison.
  • dy/Y is the ratio of the change in a measure over the total, like an interest rate or growth rate measures.  I get smoother curves by blending a bit, using a center-weighted 5 point bracket.

Data Sources:

  1. 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
  2. World Meat Production – 1961-2016 Rosner – OurWorldInData: https://ourworldindata.org/meat-and-seafood-production-consumption
  3. World Food Production – 1961-2016 FAO: http://www.fao.org/faostat/en/#data/QI
  4. WRI has a very nice set of data on CO2 “Total GHG Emissions Including Land-Use Change and Forestry – 1990-2014” from the CAIT Climate Data Explorer – http://cait.wri.org/historical.
    – Because the latest economic CO2 emissions data is 2014 not 2016 as for other data, the trend of atmospheric CO2 from CO2.earth 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
  5. Atmospheric CO2 PPM 1501-2015                    
    OurWorldInData.org:
    https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions
    From 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.”
  6. 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
  7. 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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Is Science Coming Full Circle??

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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.

Preface

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

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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[1] 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[2].  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[3]

1.   See where hidden connecting events shifted the flows??

test sext
[The missing data is about the unstable states, the markers of whole system change in design]
Continue reading Is Science Coming Full Circle?? →

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Guiding Innovative Change – Holistic applications of the SDGs

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Re: 18 – 21 Oct 2016    Addis Ababa, Ethiopia (research ref’s at the bottom)

Fourth meeting of the IAEG-SDGs

SD indicators need one more, the World SDG
so Innovators can design their goals
in relation to the whole

My comment is as an expert on both system design and natural science indicators, on how innovative organization develops in both natural and intentional complex systems.   There is a great depth of professional design practice that has yet to be consulted regarding the plan for the SDG’s

The general model of innovative transformations is that the emerging culture change, starting from some “seed pattern”, and then going through the classic phases of their own life-cycle of internal growth and changing roles in their environment (fig 1).  There are of course many kinds of invasive systems and life-cycles.   The type we are most often concerned with innovative transformations of human design, whether our own educations, or our society’s struggle to become “sustainable”, succeeds or not.

The earliest visible pattern is the emergence of an “inspiration” or “design”, looking for an opportunity to take hold, to have a starting organization that gets going by using environmental energy for building up the design.   That energy flow for formation then tapers off as the transformation progresses, toward refining the “new capability”, or “new culture” or “new business” etc.

The natural goal is generally to stabilize the design as it begins its real work at a peak of vitality, beginning a long productive life.   So in general, it’s to first grow and then make a home, to have a life.   This model developed from study of natural change patterns , applying constraints of physics principles for energy use, that for designs to develop or change they need to develop new energy uses too.

Fig 1. The stages of organization to build systems and their energy uses
Fig 1. The stages of organization to build systems and their energy uses

I’ve been attending the UN SDG meetings for four years, first for the Institute for Planetary Synthesis, and then with CIVICUS, learning a tremendous amount, but also noticing the very distinct lack of systems thinking in the design of the SDG’s.  The main reasons seem to be that systems thinking is not taught in liberal arts educations, and that the design of the SDG’s was mainly shaped by demands for change, by issue focused groups from governments and civil society, not experienced with how organization relies on designs to join differentiated parts.   So ideas of how to organizing the differentiated parts when undiscussed and were mostly left out.

So the process produced 17 idealistic “goals” and 36 main “topics” discussed mostly separately, arising from a profound concern with the whole global pattern of culture change and economic development. Personally I had a wonderful time, but was also sad I never got to talk about my main expertise, i.e. on how the parts of whole systems connect.   From a natural systems view the SDG’s may be spoken of as separate,  but are all indicators of “holistic cultural growth”.   They’re not really indicators of “economic growth”, as it’s whole culture growth that brings value to an economy not the reverse.

With the process lacking systems thinking resulted in missing systems indicators: for how differentiated parts connect, for how cultures develop unity and cohesion.   The diagram below is mainly for study, a “sense making tool”, a “map of questions” to help guide innovative changes.

The challenge is our usual mental confusion, with our minds working with disconnected bits of information and but actually working in holistic organizations and trying to engage with holistic systems of our world.   So our “maps” and our “worlds” show a “mismatch of variety”.   So we need to constantly study and learn from new experience.    To succeed with an SD partnership, the organizers first need to find a “start-up match” between its “own abilities” and “an environmental opportunity”.  Usually it takes “a study of the context”, identifying “forces to make whole” with a “unifying response” ( a reference to “pattern language”) .   In terms of the 8 kinds of indicators for planning change, it’s matching type IV indicators of whole system potential, one set within the organization and the other in the environment.   The actual initiative might focus on one or the other…

The 4 quadrant map  has “condition indicators” for “states” (how things are) and “guides” (what can change).  It has “context indicators”,  “local” and “global”.   The four quadrants are repeated for the Organization and the Environment as a 3rd dimension for the array.    This arrangement borrows a bit from David Snowden’s Cynefine “place” centered holistic complex system business design practice.    It fits with the long lists of indicators of functionally different kind needed for the SDG’s

There are also other advanced holistic system design traditions to choose from.  In all of them design proceeds in “stages” of team “learning”, “work” then “review”.   With each cycle all the indicators being worked with are reviewed.   All the indicators the organization uses to guide it are consulted in the learning phase of each cycle.    The architectural, product design and performance design professions have ancient traditions of how they do their work.   Newer traditions of system design where this kind of learning is studied include “action learning”, “pattern language”, “object oriented design”, and “permaculture”.   None of these traditions of advanced design practice seem to have been consulted for the SDG’s for some reason.

Fig 2 Three dimensions of planning for innovative change, Organization & Environ, States & Guides, Local & Global
Fig 2 Three dimensions of planning for innovative change, Organization & Environ, States & Guides, Local & Global

 

I do hope the above is helpful
for where SDG implementations can go for advice.

My real reason for writing, …and offering this way of understanding transformational change,… is the oddly disastrous pattern of excluded indicators in the official statistics for the SDG’s.  The measures of ESG impacts that businesses are told to report as measures of their responsibility, have many more exclusions than inclusions.

It is possibly unintentional but oddly very boldly “hidden in sight”, the clear exclusion of all responsibility for the disruptive impacts of business and investor money decisions.   It comes from the modern continuation of the ancient practice of excluding all business responsibility for economic “externalities” of the choices for what to profit from.   Some impacts of what to profit from no one in the past would have know about.   Now we really do know most of them.

The very largest exclusion from business impact reporting, though, is one that anyone would always have known about.   It’s all the human consumption that business revenue pays for to obtain human services, ALL of it, as if those impacts had no environmental cost.   That one accounting exclusion is commonly five or ten times the impacts the rules say businesses should count.   The indication is that we have not started doing any form of sustainable development yet, systematically making decisions as if 80-90% of the impacts don’t exist.

At the UN and in writing to people I’ve been finding most people understand all this fairly quickly, …but then avoid engaging in discussion, the worst of all possible responses for our world.   The cover-up and avoidance is always the bigger crime.

I urge you to respond to the challenge.

There’s a simple way, too,
include in SD reports one new indicator,  “global share of GDP impacts” proportional to share of global GDP

It’s really important to start the discussion.

Thanks for all your dedication and work
Most sincerely,

 

Jessie Henshaw

______________

The next more detailed introduction,
to the “mostly uncounted” SD impact indicator problem, with references.

fyi  –

I’m writing as a scientist, and expert on the design of natural systems and natural science indicators.   I had wanted to attend the Ethiopia EAG meeting on Indicators, due to the major neglected issues I need to raise.   Not having a sponsor I thought to pass on some of it to others who may get there.   It’s about reliable filling the unusually large gaps in the SD impact indicators used for decision making.

As a consulting systems scientist I’ve has been attending UN meetings for four years, observing the SDG process, and noticing the big gaps in systems thinking being built into the plan.   One in particular is that our impact measurement methods are not holistic, but actually quite fragmentary.   Just having better information on visible impacts won’t tell us about the growing system-wide  impacts, so SD decisions will still be unable to avoid traditional pitfalls of economic planning.   Going ahead with just fragmentary indicators could really then make the SDG effort backfire, perhaps badly, adding to the “externalities” of the economy not reducing them.

That we are not yet doing holistic impact assessment is fairly easily documented, as whole categories left out of the accounting.  There’s  an amazing list of things the economists (at the direction of the OECD it seems) have arbitrarily left out of the list of things to count.   The peculiar result is that the exclusions add up to nominally 90% of the real total.  The biggest category of exclusions is usually the largest category of business environmental impacts.  It’s the impact of paying business people for their human services, and for professional services, financing and public services.  As a result SD decisions to maximize profit are being made unaware of nominally 90% of the future impact costs of those decisions.   It’s surely a long standing habit we can’t change all at once, but we desperately need a recognition of it.

The economists have historically counted the business impacts as only things the business specifically directs.  That then treats the “consumption for production” of human services as having zero impact, the usual largest of costs and of lasting environmental impacts of any business.  The same is the case for all other supply chain impacts that are packaged as “services”, all counted as having zero environmental impact..    Having so little information on the lasting direct costs of business profits has always been a problem, and when combined with not feeling responsible defining “business as usual”.   Today SD decision makers are still trying to maximize returns with a similar lack of information, though, as if just feeling responsible would compensate for the misinformation.   It doesn’t.

I think most important is not to pick fights but to raise discussions of our common responsibility to address our common interests, to begin to include ones we’d been blind to.  The caution is that It’s common for people whose sight is suddenly restored to be in shock, so it’s caring for them not making demands that lets them see.

If you or others would like to follow this up, you might start from watching my video comment to the UN on July 11 (1), and read the short  “Impacts Uncounted” circular (2).  I found it very effective for explaining the details when talking with people at the UN.    There’s also a quite surprising scientific solution that makes holistic accounting possible, first reported in a peer reviewed 2011 paper (3).  How to use that principle that “shares of the economy are directly responsible for shares of its impacts”, because of globalization, actually, is shown in a general 2014 proposal to the UN called the “World SDG” (4).   It’s not getting discussed much yet,  apparently due to the shock.  Another caution, of course, is that we need the old economy to build the new one, part of why transformations are complex.

The big mental shock seems to be realizing the lasting impacts of using money are not close to “zero” at it appears.  They’re actually very likely close to “average”, for being so unusually widely distributed the way an efficient economy works, that to do most anything takes everyone’s service.   That “reassessment” is an almost infinite change of scale in our responsibilities, after all.   It directly connects what we do innocently with money with all the disruptive things the economy increasingly does as our growth model collides with the limits of the earth,  ..hurting the distressed communities the most.

So what we need is for people to keep doing what they’re doing, and begin to assume they have a real responsibility for what’s going wrong with the economy and the world, in approximate direct proportion to their share of the economy.

 

I hope that connects with your thinking and gives you a start with mine.   Please send me anything you think is relevant.

Good luck your good work!  Thanks so much for your time.

  1. JLH at UN HLPF – comment on Growth & Impacts Uncounted, 11 Jul 16
    – https://www.youtube.com/watch?v=CxSmEixz5WQ
  2. Impacts Uncounted circular
    – http://www.synapse9.com/_SDinteg/ImpactsUncountedl.pdf
  3. Henshaw et. all. 2011,System Energy Assessment (SEA). Sustainability 2011, 3(10)
    – http://www.mdpi.com/2071-1050/3/10/1908/
  4. World SDG proposal
    – http://synapse9.com/signals/2014/02/03/a-world-sdg/

 

JLH

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NYC data Science… examples

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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 →

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Scientific Community on Natural Limits

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Post event note:

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:

S D   M e t r i c s   L e a v e   M o s t   S o c i e t a l   I m p a c t s   U n c o u n t e d  

It’s to say:

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:

Friends,

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!

Jessie

 

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A pitch for introducing bigdata “system recognition”

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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

___________________________________________

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

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16 Tweets on Reading #BigData for Life

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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

  1. What we talk about becomes society’s reality, so we can read #BigData for what’s happening #following_all_cultures and #resources_on_earth.
  2. And what may matter most in #BigData is going from reading abstract patterns to reading naturally occurring ones. http://synapse9.com/jlhCRes.pdf
  3. Then add the magic of learning to read the patterns #BigData reveals, as exposing the designs of the natural systems producing it.
  4. Reading #BigData for natural patterns shows you even the best data doesn’t show what systems are producing it. 
  5. 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
  6. If our world #economy is causing trouble for the #earth, why do we think it helps to speed it up? #Get_real_people!

    Escher
  7. Are @google, @IBM or other #BigData #research teams learning how to read design patterns of natural systems?? http://synapse9.com/jlhCRes.pdf
  8. To start reading natural systems in #bigdata look for cultures made individually, clustering or growing from seeds.

    from PURPLSOC 2016 http://www.synapse9.com/pub/2015_PURPLSOC-JLHfinalpub.pdf
  9. Then follow recognizing nature’s cultures with learning from them, going back and forth between models

    from PURPLSOC 2016 http://www.synapse9.com/pub/2015_PURPLSOC-JLHfinalpub.pdf
  10. When reading #bigdata for behaviors of cultures also note contradictions in the news, like #jobs_going_to_Mexico and #refugees_escaping_too.
  11. #BigData exposes surprising whole system views too, #professionals managing systems of growing inequity, disruptive change and impacts too.
  12. #BigData reveals living cultures: business, economic, social, biological or ecological, etc. all either: homeless, home seeking or enjoying.
  13. 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
  14. 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/
  15. 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
  16. 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.

jlh

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at PURPLSOC, then at PLoP: Pattern Language for Object-Oriented Science

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…. The distinct possibility is that, for the first time:

Science might soon be able to study all the objects of nature, in their innate form
not just the models we make based on what data is available…

______________________

In-depth Pattern Language Research
“Guiding patterns of naturally occurring design”

1) For PURPLSOC 2015: on “Elements”  (final for publication)
2) For PLoP 2015: on “Mining living quality” (meeting draft)

                                             ______________________

To recognize

  • 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:

Pattern Language becoming a general language of object oriented thinking and design in all fields.

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).

“Guiding patterns of naturally occurring design”

1) For PURPLSOC 2015: “Elements”  (final for publication)
2) For PLoP 2015: “Mining living quality” (final for publication)
3) Related materials: Resource directory 
4) 20 min. YouTube video excerpt of the July 5 talk –
5) “Whole Systems Energy Assessment (SEA)…” part of the physical systems science being translated into PL 

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Need to update & add notes and discussion on both conferences….

It was really exciting 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“

JLH  11/5/15

Policy discussion, Research & Measures, Transformation, What to do

Are we protecting our symbols? or protecting the earth?

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Warm greetings, real math.

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We publicize the “special places” that are threatened, and people respond, yes.   But we have to face that after 100 years of doing that, the environmental movement that our conservation groups and actions have been at the center of, has protected lots and lots of **special places** but is still not protecting the **ordinary places**.

Protecting thousands of special places, the ordinary places left unprotected

The effect of our organizing has been as if we didn’t know the ordinary places were just as threatened as the special ones, by the same visible and ever expanding encroachment from our economy.  Don’t we need to get that straight?   Don’t we need to be much more direct in saying that the threats to the special places (that get everyone’s attention) also symbolize the threat to the earth as a whole?

Places like Tucson we haven’t protected

If we do that it could materially change the common goal, recognition that really save the earth we need to **remove the threat**, not just **protect the things that symbolize the threat**.   Isn’t that a change in view we need to bring about?

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