Category Archives: Natural patterns

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

UN SDG’s and HLPF – the whole-system thinking needed

Setting Our Whole system goal,
Making the Earth our good home.

Much of my effort over the past five years fosuces on working with civil society organizations at the UN on the world sustainable development goals (SDG’s).   This year, needing to take care of other business, I’m sitting out.    It may be ironic, of course, as the challenges of implementing the UN 17 separate goals probably makes:

more and more participants think of how the goals need to all work together,
and can’t individually be achieved without the ‘nexus’ of the whole. 

Ultimately my years at the UN was mostly spent identifying the widespread absence of systems thinking in the SDG’s, watching somewhat painfully as the UN spent all its time creating lists of separate goals, as if unaware of their interlinkage. The interlinkage most neglected of course was that of *MONEY*, our main tool and problem.   So my writing of that time may be a little out of date.   I can tell that much of the systems thinking I found so absent before still is, however.

I have lots of other writings on how “systems thinking” for our world needs to become “systems making”, the next step toward true “homemaking on planet earth”.   Finding how societies can make their good homes on earth is the answer.   Like ecologies of other kinds do, we can invent our way out of the deepening trap we now find our world economy in, using nature as a guide.

I got fairly frequent applause at the UN,  enough to know people are listening, but to my knowledge no one ever followed up on my carefully  reasoned recommendations, and no one ever asked me to be on a panel discussing them either.   So here I’ve collected some of my old lists of observations on the process, and reiterate my offer to help people understand the guiding patterns of natural system design that I’ve spent my life studying.

A Youtube of one of my interventions for last year’s HLPF gets right to the heart of the matter too!

A good Youtube – Impacts Uncounted 2016 UN HLPF S4

 

Notes on how the “UN Development Goals… leave out the Common Needs”(1)

  1. The SDG’s Omit:
    1. how the centers of power can become foundations for a sustainable future
    2. what habits keep people entrapped in the web of ever growing economic inequity.
  2. It also fails to use systems biomimicry to help us design a world that works.
  3. They also ignore the need for a “soft landing” for the whole system, for
    a. creating a stable world commons…
    b. not just to giving competitive edges to the disadvantaged
  4. They don’t aim to “internalizing all externalities” like the “World SDG” offers.
  5. They also generally omit ideals for the earth as a whole, to make it our good home.
  6. and blindly continues to promoting compound global growth
    a. when we are already pressing hard the global natural limits,
    b. not looking to relieve of the strains but add to them.

 

Here are other short posts on the systems thinking to bring into the UN’s discussion to guide us toward the long term goal of making Earth our good home:

  1. http://synapse9.com/signals/2013/07/01/un-devel-goals-omit-common-needs/
  2. http://synapse9.com/signals/2013/04/27/missing-ecological-thinking/
  3. http://synapse9.com/signals/2013/05/05/whole-culture-led-not-tech-driven/
  4. http://synapse9.com/signals/2013/12/05/un-owg5-missing-topics/

 

JLH

Why is an economic tornado always on the road straight ahead?

I also can’t help returning to a central subject of collective organization I’ve studied my whole professional career, the seeming fate of economies to bring periods of high cooperation to an end with total disaster.   The main cause could of course be said that no one in particular is at fault.   But there is science enough to identify who could intervene, and do something about it.

My previous post was on the work of  Ernst Ising, the physicist who solved a range of collective behavior problems, and how pattern language design science might address the question of what kinds of environments are required for emerging local phenomena.   Why economic collapse is always on the road straight ahead for our form of highly cooperative modern  economies is one such subject I’d like find physicists using Ising’s work on with.

One might wonder about what keeps driving our highly cooperative world economy toward escalating conflict.

All of humanity seems driven by a “rat race” toward extremes of destructive competition all the time, unable to escape, with most everyone feeling they are reacting in their own defense.   That’s not a model for a safe and secure world.

Could we possibly trace how the economic forces, like those driving everyone to achieve rapid growth in economic productivity, and so for the earth and humanity, creating circumstances ripe for triggering grand economic collapses.   If we can identify the system doing that we could identify interventions well in advance, to engage a “general protection fault” to avoid the usual mad collective collapse.

Why is an economic cyclone ALWAYS on the road straight ahead

 

I for one think it boils down to demanding people do impossible things, demanding of our society to do impossible things, like continually doubling the speed at which we collect and use energy and expand our control of the earth.   That can only end in tragedy, like it has for economies again and again. Why economies are driven to it, to be ever more productive at ever faster rates, follows unavoidably from their organization for maximizing compound returns from investment, making ever more from ever less.   Like being forced to “make bricks without straw”, the regular investment of profits in escalating to create ever more daunting competition ultimately compels cooperation in cheating.   In the end that unavoidably disrupts the order, as one of the natural outcomes of pointlessly taking the compounding of returns to its natural limit.

We could do something else if we understood the problem…

 

jlh

The duality required for collective organization.

An interesting global question is, to me, raised by Ernst Ising’s work in physics – (see the arxiv pre-print on his life and work if interested. https://arxiv.org/pdf/1706.01764.pdf)

Ising’s main work in the 1920’s was deriving a mathematical explanation for ferromagnetism, the ability of atoms in certain solid metals to develop aligned spins, and exhibit permanent magnetic fields in there surroundings as a result.   The part of that might be of interest from a pattern science viewpoint is how his model has been successfully applied to numerous collective phenomena, both other emergent collective atomic behaviors like magnetism as well as emergent collective macroscopic behaviors like the emergence of organization in crowds.

Ising’s general equation

The math, honestly, is beyond me, but there’s an interesting assumption in the work that might be discussed from a pattern science perspective, that the math rests on treating such phenomena as arising from purely local interactions.

Ising Saying:
“So, if we do not assume [ ] that [ ] quite distant elements exert an
influence on each other [ ] we do not succeed in explaining
ferromagnetism from our assumptions.  It is [thus] to be expected
that this assertion also holds true for a spatial model in which only
elements in the nearby environment interact with each other.”

What I suspect is that there’s more of a wave/partical type duality present, involving both local and contextual interaction
in bringing about collective organization.

In the collective phenomena we observe there is certainly has a strong local character, whether it’s snowflake formation, ecologies, social movements or probably also the punctuated equilibria of emerging species.  All such collective phenomena seem to arise in relatively small centers and then spread mysteriously.  They also seem to require specially primed and fertile environments, as global conditions that are receptive to the local accumulation of collective designs.

So my question is who else is talking about this pattern of nature.    Is this raised in Christopher Alexander’s “The Nature of Order” or other pattern language writings?   Is it raised in the work of anyone else writing in the pattern language field?  More specifically, does it need to be understood to know how to describe the contexts we work in, perhaps such that a calm and receptive and so fertile context is needed to be a good host for pattern designs to flourish?

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jlh

Is Science Coming Full Circle??

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??

Regions Left Behind

The discussion of the UN’s  Sustainable development Goals (SDG’s) focuses on the poor, and “Leaving No One Behind”.   That overlooks that it’s most often the growth of the world economy that made older parts of the economy outmoded, and leaving whole communities behind as the world economy moves on to what’s more profitable.  This discussion illustrates more of the detail, how innovative change like the “green revolution” thought to be for feeding the poor.   It would quite predictably also leave more and more agricultural communities behind, …as everyone has increasingly seen in their own regions… like in my own home region of New York State, exhibiting common symptoms of being economically left behind you see around the world:

  1. abandonment of rural communities
  2. as farmers can’t afford sell to feed their own communities
  3. the flight to cities with now skills to sell
  4. the growing refugee and landless migrant populations
  5. growing youth cultures with little to do but to get angry
  6. or that are fighting over resources degraded by over use

And that’s only one of the kinds of distressed communities unable to keep up with the competition ans the most profitable invest their profits in becoming more profitable and more and more people can’t keep up.

A natural pattern of the growth systems is using profits to develop innovations that are more profitable, which of course also multiplies the disruption of the ways of life being displaced..., that we've called
1. & 2. A natural pattern of the growth systems is using profits to develop innovations that are more profitable, which of course also multiplies the disruption of the ways of life being displaced…, that we’ve called “externalities”, as if they didn’t matter, and don’t get counted. It leaves more an more communities behind at the limits though.

These all actively leave whole societies of suffering people behind in a way that is not reversible.   It’s the real predictability of ever escalating competition causing all these uncounted impacts of how we invest money in growth for the wealthy, that undermining the sustainability traditional economies.   That’s the real quandary here, it so very predictable.    What DO development planners think about, not to ask who the latest innovation will put out of business.   Well, to do real sustainable design, we’d need to add that question to the list, what will our “killer app” put out of business?    It’s always a trade-off when you “create jobs” of any kind, that there will be jobs lost elsewhere with a very high probability.

Conceptually the lasting profitability option is fairly simple, gradual stabilizing of the whole system profit as the profitability of growth stops growing as fast, leading to a steady state creative living.

The Lasting Creative Spira, so familiar in life, only requiring that investment not be compounded as growing innovation meets diminishing lasting returns.
3. The Lasting Creative Spiral, so familiar in life, only requiring that investment not be compounded as growing innovations meet diminishing lasting returns.

________________

For the big picture of how we got the math wrong…

The economic impacts we don’t count turn out to be the great majority of disruptive earth and societal impacts we experience (seeImpactsUncounted ).  They even have a name, the “externalities” incurred as liabilities of obtaining services by paying someone else to deliver the goods.   So those are actually internal to the operating necessities for running a business, only external to the accounting we’ve been doing.   Counting them is actually just ruled out for SD accounting, by a “stroke of a pen”, as effects that decision makers don’t feel responsible for, and have no direct control over.   Those include impacts of financial decisions, for investing in disruptive innovations, also excluded from the discussion of impacts by the stroke of a pen.

Some impacts of finance are easily measured and some not, so to fully understand the problem takes sorting through what is accountable and what is not, develop different ways of assigning shares of responsibility.    Certainly the ones that are measurable should be counted.  They’re mostly counted globally, like soil and water depletion and lots of other things.   They’re just not at present assigned to anyone’s responsibility.  Doing so proportional to share of world GDP would be both scientifically correct and perfectly fair.   So a study group would pick one or two such questions at a time to see what can be learned.

Another concern is how the continual compounding of profits forces everyone in the economy struggle to keep up with financial demands for ever increasing productivity and competition…  what the phrase “the rat race” technically refers to.   It’s why we all seem forced to to run ever faster to stay in one place.   That’s of course not really sustainable, but very hard to know how to measure.   Still, it’s a very real kind of suffering and accumulative culture change, and connected to the escalating competition in the economy that leaves ever more people and communities behind, a kind of “destructive creation”.

In figures 1 & 2 illustrate how regions are left behind, using the example of how once thriving agricultural communities of New York State collapsed, leaving long term economic damage behind.   The question is where did the money go that once invested in productive farming in the region.   The costs were left behind as the money fled to create the extractive industrial farming of the mid-west and elsewhere, mining water and fossil fuel resources very unsustainably, to grow corn, wheat and soy where it wouldn’t thrive naturally.  Of course, much of this is only observable in hind sight and not really manageable, but the costs to society clearly also do escalate.   That makes it imperative we take responsibility and do what’s right.    The driver is making more profits, for investing in even more competitive businesses, using “disruptive innovation” that also leavea ever more others behind somewhere too.

For many decades people have more often called that effect of disruptive innovations “creative destruction”, accepting that to make more money and increase the economy’s products, you have to destroy the economy’s old ways of making products.     The hard question is when to change from calling that  “creative destruction” to calling it “destructive creation”.    The programming of the economy to always grow that process seems to assure ever stiffer competition for everyone, all the time.    It’s so constant we might just take it for granted,… but as a continual culture change for pushing everyone to face ever stiffer competition for how they live, it’s certainly not sustainable.   As you push the limits then… it seems to naturally leave more and more people and environments behind, and be really more destructive than productive.

How that escalates is illustrated below, alongside the map of New York State, roughly showing the area of Central NY farming communities that vanished in the 50’s to 70’s, giving in to the competition from industrial farming.    We could count the region’s lasting economic and cultural damages, perhaps.   We can also see that the global corollary is of larger scale and seeming leaving more and more behind around the globe all the time.   We can see it was no one’s political decision, nor is there anyone else at direct fault.  We can see that kind of change is quite irreversible once it has happened.    We’d only know if we counted it, and attributed the costs to our financial decisions to profit that way.   As societal collapses are not reversible, we’d really need a more holistic way of measuring our impacts, to understand the costs of how we make money for our future.

Its SO predictable!

What would make people care??

What would let them notice??

Guiding Innovative Change – Holistic applications of the SDGs

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

Scientific Community on Natural Limits

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

 

Next thing to “make it in NY”?

Short version Voted a Top Comment on the Forbes article
The Stock Market And Bernie Sanders Agree — Break Up The Banks” ,
a
 more full story follows.

The reality of the matter is as embarrassing as it could be. If you trace it all back to origins… it’s our very own greed causing the whole mess, our demanding that Wall Street produce ever faster growing **unearned income** for our investments.

That’s what is now backfiring on us as the serious scientists all always said it would. The earth is not an infinite honey pot… is the big problem our not so big hearts and minds have in grasping the consequences of our own choices. We simply failed to notice the consequences, or listen to those saying “beware of what you ask for”.

The truth is WE became “The Sorcerer’s Apprentice”* and now we are dealing with having turned the planet into our Fantasia. The truth is that if we “Break Up the Banks” the financial system we designed to grow unearned income will just keep multiplying the disruptions the scientists always pointed to it causing! Are there options?? Well find someone honest who studies it perhaps…

 Sorcerer’s Apprentice http://goo.gl/Zu69yD
(If  this YouTube copy is inaccessible sometimes you may need to find another copy or just recall the heroic tragedy of it all, from the last time you saw it.)

Day after the NY Primary 2016:

In New York State yesterday there seemed to be a lot of answers, but we can all see more questions too. Neither Trump nor Sanders are offering practical ways of doing it, but clearly raised a huge chorus of “throw the bums out”, without actually identifying “who the bums are” as part of the questions left hanging. To the surprise of many Trump’s win was so persuasive it seems to almost legitimize his candidacy. To the surprise of many as well, Sanders overall persuasively lost to Hillary Clinton, and only had persuasive wins in conservative upstate areas. In ultra-liberal New York City, his claim to ultra-liberal leadership found really very few neighborhoods persuaded. New York is the kind of place that needs no persuasion at all on the legitimacy of his issues, but found his manner and inability to say what he’d actually do, and relying on a constant stream what had to be called rather misogynist digs.. caused him to lose legitimacy.

So nearly all agree the bums need to be thrown out, but “who the bums are” remains unanswered, and largely undiscussed too, The Trump campaign colorfully claims the intention to disregard all the rules to “get the raccoons out of the basement”, and with no strategy but public outrage, sweep away the broken Republican party and Washington DC political establishments. Sanders imagines that some executive order breaking up the banks and popular demand for relieving very real and widespread despair will remove all the barriers to doing that.

I’ve studies these problems in great detail for many years, and have in fact been expecting to have to somehow claim to have predicted this kind of grand societal collision with itself from the first time I caught a glimpse of the real problem. My observations are only a little more detailed and focused on locating who has a choice, who actually is “at fault” in that sense, as the natural disaster at the end of capitalism has been has been long predicted for what I see as all the wrong reasons for centuries.

That real problem is that “Wall Street” is the name given to the practices of the financial traders who trade everyone’s investment funds, and so… “Wall Street” actually already works for us, and doing precisely what we ask it to do. There’s just something profoundly confused about what we ask it to do. We ask it to manage the use of our idle savings to produce profits to add to our savings, and so multiply in scale without end except for letting the trader take a share of the spoils, Of course the bargain is that multiplying your profit taking from your world with no exception eventually destroys your world, invisible only if you don’t look.

I don’t know quite why Goethe did not sharply identify that ultimately seductive bargain with the Devil when writing Faust. That play is apparently his morality tale about what happens when making that bargain. He was, though, enough more clear in depicting it in his balladic poem Der Zauberlehrling, that Walt Disney used as the basis of his ever popular animated film Fantasia, and very pointed fable “The Sorcerer’s Apprentice”.

Our hero, Mickey Mouse, steals a look at the sorcerer’s book of secrets and immaturely calls upon its magic to command his broom to carry the heavy water of his chores, so he can sleep all day. As he awakes he finds the magical broom can’t be stopped, as Micky doesn’t know what spell to cast for that, and is flooding the whole house and castle, and so MUST be stopped. Then like people feel today, Micky picks up his ax to do in the boom for good…, but finds in chopping up the one it only multiplies magical brooms and the rising flood turns into a great torrent.

As Mickey sleeps his magic brooms multiply, and his effort to chop them up has the opposite effect, not knowing the magic to make them stop.

The failure of Mickey’s strategy would, of course, be repeated if Sanders’ grand gesture calling for “breaking up the banks” were to actually be applied. The various banks that have now grown overwhelmingly big, magically carrying our water so we can accomplish ever more without work, will all just continue expand, as long as we ask them to use our savings as before. You would just get more banks accumulating more disparity in the wealth of the world. Whether the phrase “break up the banks” refers to dividing up the banks into smaller ones, or separating their savings and investing functions, it wouldn’t alter a bit the basic service they are being asked to provide us as investors. They’d still be using our idle money to multiply, in some magical way, so we can be showered with fruits without labor, and left with the puzzle of why that can’t keep working.

Investors may or may not feel “wet”, but if you look around the world, everyone else does look rather soaked! It’s a quandary that we’ll have to resolve, why the secrets of creating wealth were apparently not shared by our process of enjoying wealth. So what’s clear, at least, is we now have a new job. It’s not one that Wall Street asked for, perhaps, but that they can’t refuse as they work for us. It’s to break with the Faustian bargain we made with ourselves, and perhaps stumbling some also stumble without regrets so much as anticipation, get about the work of showing the world another side of what we can do with our genius.

Here we don’t find ourselves without a plan of action, is what’s different from the many calls to protest, though the plan may need repeated adjustment and improvement in various ways. It’s ironically not like Bernie’s plan to “not take Wall Street’s money” either. It’s indeed to “take Wall Street’s money” we belatedly realize, because Wall Street is in fact just managing our money for us, and we just need to as for the right thing. That’s the real way to break our bargain with the Devil, that we do seem to be at a great historical point of rejecting. We can take our knowledge of wealth with us too, but only if we learn the other tricks needed to leave the earth whole and to share.

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

___________________________________________

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