"One Guy With a Marker..."

My granddaughter sent me a link to a video that claims:

“One Guy With A Marker Just Made The Global Warming Debate Completely Obsolete.”

I watched the video with interest. He says he has: “… An argument that leads to a conclusion even the most ardent skeptic and most panicked activist can agree on. No one I’ve shown it to so far has been able to poke a hole in it. …”

I found some logical errors in that video and posted This Topic to the world's most viewed climate website where it attracted some 17,000 page views and almost 200 comments, including this one:

[From John Coleman, Co-Founder of The Weather Channel]

Ira, please make a video that presents the counter argument so I can put the two videos together for a You Tube “gotcha”. This guy is getting lots of hits and needs to be answered on You Tube.

So, I made the suggested video, and here it is:


When John Coleman viewed my video he wrote:

Wow Ira. You did it and you did a great job of debunking the marker nut. I hope you get a lot of hits on You Tube. It is a huge job to debunk the bad science. We should all keep on trying every day. I am writing a daily blog, but maybe I need to make some new videos a well.

Indeed, within a couple days I had THOUSANDS of video views on You Tube! THANKS JOHN!
Click for John Coleman Weather Daily Blog

DECONSTRUCTION OF THE "ONE GUY WITH A MARKER ..." VIDEO




As the above frame grab from the One Guy With a Marker ... Video indicates, he divides the Global Warming debate into two extreme dichotomies:

• Global Climate Change (GCC) is “False” (Top Row) or “True” (Bottom Row), and
• We take Action “Yes” (Column A) or “No” (Column B)

Here are the results he gives for his four boxes:

1. GCC is False but we unnecessarily take Action. The result is a high “Cost” that results in a “Global Depression”.
2. GCC is False and we take No Action. The result is a happy face.
3. GCC is True and we take Action that stops GCC dead in its tracks. The result is a happy face.
4. GCC is True and we take No Action. The result is “CATASTROPHES [in the] ECONOMIC, POLITICAL, SOCIETAL, ENVIRONMENTAL, [and] and HEALTH” areas.

He ends with the inevitable: “The only choice is Column A” – we must take Action!


This is a great example of the well-known FALLACY OF THE EXCLUDED MIDDLE! Where EMOTION is used in place of REASON to give the viewer a false choice between two extremes.

“All or nothing”, “Camelot or Catastrophe” arguments have great emotional power in political discourse, where the (usually hidden) assumption is that some things are perfectly TRUE and others are perfectly FALSE. But the real world is mostly in shades of grey. He studiously avoids that complication, because, when shades of grey are considered, his argument, IMHO, falls apart.


Let us take a closer, more realistic look at his four boxes:

1. GLOBAL DEPRESSION: This box is included to make it appear he is being “fair” to Skeptics. He assumes that taking Action to stop GCC will be so costly that, if it turns out to have been unnecessary, the result will be a “Global Depression”. Certainly, maximum environmental spending will damage the world-wide economy, but I doubt that type of spending, alone, will trigger a “Global Depression”. When we get to box #3 we will see that he doesn’t really think so either!
2. HAPPY FACE: GCC is “False”, we take No Action, so all is well! But, is it? Does his “GCC” include NATURAL PROCESSES and CYCLES that have caused Global Warming (and Cooling), Floods (and Droughts), and Violent Storms (and Blessed Rain) prior to the advent of Humans on Earth, and before we Humans had the capability to affect the climate? Apparently not, else “GCC” could not be totally “False”. Therefore, by “GCC” he is referring ONLY to the HUMAN-CAUSED variety, totally ignoring the evidence from the geological, ice-core, and historical records of NATURAL Global Climate Change and some Catastrophes.
3. HAPPY FACE: This box is totally inconsistent with box #1! If Action to stop Human-Caused Global Warming is so costly as to cause a Global Depression in the first box, would it not also cause such a Global Depression in this box? So, why the Happy Face? Realistically, even if we in the US and other nations in the Developed World take maximum Action to reduce our CO2 emissions, it is totally unrealistic to expect those in the Developing World to do the same. Indeed, China, India, and other countries will continue to build power plants, nearly all of them coal-fired. CO2 levels are bound to continue their rapid increase for at least the coming several decades, no matter what we do.
4. TOTAL CATASTROPHE: This box is filled with terrible consequences and is intended to scare us into taking maximum Action. He assumes the worst-case Global Warming of several degrees predicted by Climate Models despite the failure of those Climate Models to predict the past 17 years of absolutely no net Global Warming. (The most realistic prediction is continued moderate change in Global Temperatures, mostly NATURAL but some small part HUMAN-CAUSED. As standards of living continue to improve world-wide, populations will stabilize which will allow reasonable action to be taken to moderate CO2 emissions, and Human Civilization will ADAPT to inevitable Natural and Human-Caused Climate Change as we have throughout history.)

Bottom Line: This “One Guy With A Marker” DID NOT MAKE “The Global Warming Debate Completely Obsolete.” His failures of logic:

• He assumes HUMAN-CAUSED Climate Change is the only kind we need to worry about, which flies in the face of the fact that most Global Climate Change has been and continues to be NATURAL, and not under Human control or influence.
• He assumes costly Action to prevent GCC will cause a GLOBAL DEPRESSION (box #1) if GCC is “False”, but the same costly Action will cause a HAPPY FACE (box #3) if GCC is “True”. Box #3 contradicts box #1.
• He ignores the fact that GCC models have way over-predicted Global Warming. For example, taking 1979 (when worldwide Satellite temperature data came available) as a starting point, the average of 102 Global Climate Models predicted warming of 0.9°C (1.5°F) by 2013. Actual warming from 1979 to 2013 has been less than a quarter of that, and there has been no net Global Warming since 1997. During this time period, CO2 levels have continued their rapid rise. (See http://wattsupwiththat.com/2014/01/12/global-warming-is-real-but-not-a-big-deal-2/)
• He assumes “All or Nothing at all” and “Camelot or Catastrophe” which is the characteristic of irresponsible EMOTIONAL argument. He ignores the shades of grey in-between. He brings POLITICAL rhetoric to what should be a rational SCIENTIFIC discussion.

Ira Glickstein

Related: See Craven Attention, where Steve Mosher reports on the "Guy With a Marker ..." making a buffoon of himself at the AGU (American Geophysical Union).

Forget CO2 - US Executions and US Debt "Cause" Global Warming

This is my latest Topic published four days ago on WUWT the world's most viewed Climate Website. It has received nearly 4,000 page views and dozens of Comments!

From the "correlation is not causation" department.
Guest Posting by Ira Glickstein Since the Death Penalty was restored in the US in 1975, the Number of Executions per Year correlates with UAH Global Temperature Anomaly better than CO2 levels! So, if we want to reduce warming, cut the rate of capital crimes! (See graphic below.)

In the above graphic, the black line indicates US Executions per Year from 1975 to 2013 (Source: http://www.statisticbrain.com/death-penalty-statistics/ NAACP LDF “Death Row, U.S.A., Gallup Poll, Bureau of Justice Statistics"), the blue jagged line indicates monthly UAH Satellite-Based Temperature of the Global Lower Atmosphere from 1979 through 2013 and the red line shows the running centered 13-month average (Source: http://www.drroyspencer.com/2014/02/uah-global-temperature-update-for-january-2014-0-29-deg-c/).

In an earlier WUWT posting I showed that Total US Debt (public and private) as a percentage of US Gross Domestic Product (GDP) correlates with NASA GISS US Annual Mean Temperature Anomaly better than CO2 levels! So, if we want to reduce warming, cut the debt! (See http://wattsupwiththat.com/2011/02/16/forget-co2-us-debt-causes-warming/)
Therefore, based on the (faulty) idea that Correlation Implies Causation, we can solve Global Warming by reducing US debt and US capital crimes :^)

Ira Glickstein

Global Warming in Perspective

GLOBAL WARMING IS REAL, BUT NOT A BIG DEAL

1) Some of the net Global Warming since 1880 is undoubtedly due to human actions. The Atmospheric "Greenhouse" Effect is real and water vapor (H2O) and carbon dioxide (CO2) are "greenhouse" gases.
2) The actual temperature rise and human responsibility for it have been exaggerated.  By their repeated and substantial "adjustments" to the US temperature record, the Official US Climate Team (NASA GISS) has admitted that it is unreliable.
3) Official Climate Theory is flawed because it is "handcuffed" to CO2 levels. None of the official Climate Models predicted the current 17-year "pause" in warming. CO2 levels continue to rise rapidly, but the predicted warming has not occurred. 

 I posted some of this material on 7 April at Watts Up With That, the world's most viewed climate website. In the first two days, it received almost five thousand page views and almost a hundred comments. Some commenters found my first chart confusing. The image above is a simplified version. I also presented some of this material to the "Civil Discourse Club" at the Colony Recreation Center in The Villages, FL on 7 April, and again at the Savannah Recreation Center on 14 April, with over 100 people at each venue. My PowerPoint Show is available for download: here

Going from left to right on the above graphic:

Atmospheric “Greenhouse” Effect, responsible for about 59°F (33°C) warming. This is the Natural Process that makes life on Earth as we know it possible. The mean temperature on the surface of the Earth is about 59°F (33°C) warmer due to Atmospheric absorption of long-wave radiation by "greenhouse" gases, and the subsequent "back-radiation" of some of this heat energy towards the Earth surface. (See my WUWT Visualizing series [Physical Analogy, Atmospheric Windows, Emission Spectra, and Molecules/Photons Light and Heat])

Ice Age Warming and Cooling, are Natural Cycles that have occurred about every 100,000 years according to the ice core records from the past 400,000 years. Since these cycles occurred well before humans appeared on Earth, they must be entirely natural. The climate is always changing, with up and down temperature jigs and jags at all time scales. The major Ice Age Cycles change temperatures over a range of about 13°F (7°C ).

Mean Global Warming Since 1880, According to the Official NASA GISS and IPCC Accounting, is 1.4°F (0.8°C). According to the IPCC, the majority of this Global Warming is due to human activities (mainly unprecedented burning of fossil fuels and land use that has reduced the albedo of the Earth). I have interpreted "majority" to mean about 70% and have therefore allocated 1°F (0.6°C) to Human-Causation and the remaining 0.4°F (0.2°C) to Natural Cycles.

Please notice that the Human-Caused warming is a minor uptick in temperature compared to the variations due to the Natural Cycles of Ice Age warming and cooling and the major Natural Process warming due to the Atmospheric "Greenhouse" Effect!

Even that small amount of warming is most likely exaggerated and NASA GISS and the IPCC have the proportion of human-caused and natural backwards. Actual warming is most likely about 1.0°F (0.6°C), with about 0.8°F (0.5°C) due to Natural Cycles, and the remaining 0.2°F (0.1°C) due to Human-Causation. 

Move from New York to Florida, My wife and I experienced considerable warming when we retired from full-time employment and moved from Upstate New York to Central Florida. The average temperature in Florida is about 20°F (11°C ) warmer than that in New York. While not exactly "Global" Warming, this warming was certainly due to our Human-Caused decision to move south, and, of course, we enjoy the resulting moderately higher temperatures :^). Our voluntary move caused many times more degrees of warming than the global warming since 1880!

Yearly and Daily Warming and Cooling:  Natural Cycles of the YEARLY 43°F (24°C ) temperature range in Central Florida (July mean minus January mean), and Natural Cycles of the DAILY 19°F (11°C ) temperature range we experience here. Please notice that these ranges are much larger than the Natural Cycles of the Ice Ages, and they recur on a daily or yearly basis in the temperate zones of the Earth. Again, these daily and yearly temperature cycles cause many times more degrees of warming and cooling than all the global warming since 1880!


THE OFFICIAL TEMPERATURE RECORD IS UNRELIABLE


The blink chart below illustrates how US thermometer data from the early 1900's to 1998 has been "adjusted" by NASA GISS to COOL DOWN data prior to the 1970's and WARM UP data after the 1970's. Please note that 1998, the warmest year of the 1990's, is now shown as warmer than 1934, the warmest year of the 1930's. When the same chart was originally published in 1999 by the same US-government-supported institute, the relationship was reversed. The net change in this relationship is 1.154°F (0.641°C). Both charts were downloaded in March 2014 from http://www.giss.nasa.gov/ 


We know that the US thermometer record is so unreliable that it has had to be "adjusted" several times by the official US Climate "Team" at GISS, see The Past is Not What it Used to Be, and Skeptic Strategy. and Climate Change Controversy.

Unless we believe that the world temperature record is more reliable than the US record, it is likely the world record has also been similarly "adjusted". Therefore, I have discounted the GISS estimate of Global Warming by about 30%, so actual warming is about 1.0°F (0.6°C). I believe the IPCC has over-estimated Climate Sensitivity by a factor of two or three, so I have allocated the majority of the warming 0.8°F (0.5°C) to Natural Cycles, and the remaining 0.2°F (0.1°C) to Human-Causation.

PLEASE NOTE: I do not claim that the "adjustments" are wrong, only that the underlying temperature data is unreliable. If that data was reliable, there would be no need for the repeated re-analysis and "adjustments" that have been done by the official Climate Team.

OFFICIAL CLIMATE MODELS HAVE FAILED

We have had good satellite records of Global Temperatures since 1979, so we no longer need to rely on the unreliable terrestrial temperature record. As shown in the chart below, the satellite record (blue arrows) shows moderate warming from 1979 to about 1996, and then a "pause" in warming that continues to this day. The wiggly lines on the chart show the theoretical Climate Models, with the heavy red line (and the red arrow) indicating the average of all the models. All of the official Climate Models have failed because the underlying Climate Theory is flawed.


Climate Sensitivity is the Mean Temperature Rise expected if CO2 levels double. So far, from 1979 to the present, CO2 has increased from about 330 ppmv to nearly 400 ppmv, a 40% rise. If that rate of CO2 rise continues, as it almost certainly will given the rapid industrialization of China, India, and other countries, the 1979 CO2 level will double by 2060.
 

The IPCC claims Climate Sensitivity is in the range of 2.7°F (1.5°C) to 8.1°F (4.5°C), and their Climate Models reflect that range. As the chart above indicates, when their Climate Models are run from 1979 to 2013, the expected temperature rise is about 1.6°F (0.9°C) . However, the actual temperature rise since 1979 is only about 0.3°F (0.17°C) , a fraction of what was predicted!

Based on the evidence of this chart, that the IPCC has over-estimated Climate Sensitivity by at least a factor of two to three.  I believe actual Climate Sensitivity is closer to the range of 1.0°F (0.6°C) to 2.0°F (1.1°C).



THE SCIENTIFIC METHOD vs THE OFFICIAL CLIMATE THEORY
As Richard Feynman famously said,

It doesn't matter how beautiful your theory is. It doesn't matter how smart you are. If it doesn't agree with experiment it is wrong!

The Official Climate Theory is based on a gross over-estimate of CO2 Climate Sensitivity. Reliable satellite-based temperature DATA since 1979 proves that the Official Climate Theory is WRONG by a factor of at least two or three.

According to the Scientific Method, if your theory doesn't agree with the observational DATA, you need to change your THEORY.

The Official Climate Team seems to have changed the DATA!

Ira Glickstein 

Climate Change Controversy - Global Warming is REAL but NOT a Big DEAL

The following was posted on 7 April 2014 at Watts Up With That the world's most viewed climate website. In the first two days, it received almost five thousand page views and almost a hundred comments.

Guest Post by Ira Glickstein

Some of the net Global Warming since 1880 is undoubtedly due to human actions, but how much?

[My PowerPoint Show that includes the following graphic is available for download: here: It was presented to the "Civil Discourse Club" in The Villages, FL on 7 April at the Colony Recreation Center and is scheduled for a second presentation on 14 April at the Savannah Recreation Center.]

The height of the bars on the graphic indicates the relative magnitude of Natural Processes and Cycles (in BLUE) versus Human-Caused Warming (in RED). The scale on the left is in °C with corresponding °F on the right.

Going from left to right:

The first BLUE bar represents the Atmospheric “Greenhouse” Effect, responsible for about 59°F (33°C) warming. This is the Natural Process that makes life as we know it possible on Earth. The mean temperature on the surface of the Earth is about 59°F (33°C) warmer due to Atmospheric absorption of long-wave radiation by water vapor (H2O), carbon dioxide (CO2), and other so-called "Greenhouse" gases, and the subsequent "back-radiation" of some of this heat energy towards the Earth surface. (See my WUWT Visualizing series [Physical Analogy, Atmospheric Windows, Emission Spectra, and Molecules/Photons Light and Heat])

The second bar represents the major Natural “Ice Age” Cycles that have occurred about every 100,000 years according to the ice core records from the past 400,000 years. The climate is always changing, with up and down temperature jigs and jags at all time scales. The major Ice Age Cycles change temperatures over a range of about 13°F (7°C ).

The third bar represents the Human-Caused Warming that my wife and I experienced when we retired from full-time employment and moved from Upstate New York to Central Florida. The average temperature in Florida is about 20°F (11°C ) warmer than that in New York. I miss cross-country skiing a bit, but, overall, we are happy here and we enjoy water aerobics. While not exactly "Global" Warming, this warming was certainly caused by our Human-Caused decision to move and, of course, we enjoy the resulting moderately higher temperatures :^).

The fourth and fifth bars represent the YEARLY 43°F (24°C ) temperature range (July mean minus January mean), and the DAILY 19°F (11°C ) temperature range we experience here in Central Florida. Please notice that these ranges are much larger than the Ice Age Cycles, and they recur on a daily or yearly basis.

The sixth bar represents the mean Global Warming since 1880 based on the official NASA GISS accounting. It is 1.4°F (0.8°C). According to the IPCC, the majority of this Global Warming is due to human activities (mainly unprecedented burning of fossil fuels and land use that has reduced the albedo of the Earth). I have interpreted "majority" to mean about 70% and have therefore allocated 1°F (0.6°C) to Human-Causation and the remaining 0.4°F (0.2°C) to Natural Cycles.

The seventh bar represents my personal opinion as to the actual Global Warming since 1880, discounting the “adjustments” made by the official Climate “Team” that I believe have inflated the temperature record. We know that the US thermometer record is so unreliable that it has had to be "adjusted" several times by the official US Climate "Team" at GISS, see The Past is Not What it Used to Be, and Skeptic Strategy. [caption id="attachment_40829" align="aligncenter" width="640"] 2007 email from Sato to Hansen details seven analyses of 1934 vs 1998. 1934 starts off with a 0.5ºC lead and ends up in a dead heat.[/caption]

The above GISS email from Makiko Sato to James Hansen details seven adjustments to the US thermometer record, made from 1999 to 2007. According to GISS, the very warm year 1998 was originally thought to be 0.541°C (0.97°F) COOLER than 1934, which, in a warming world, would be, let us say Inconvenient. It took multiple "adjustments" to bring them to a dead heat. Further adjustments to the thermometer records subsequent to the 2007 Sato email have brought 1998 up to a significant lead over 1934 :^).

When this email came to light due to a Freedom of Information request, it was explained away by Warmists as follows:

1) The adjustments correct for differing Times of OBServation (TOBS). OK, that could be true, but why did it take so many analyses to come to the correct result? It seems one or two would be sufficient. Also, the 1998 data has been warmed more by the TOBS adjustments than the 1934 data has been cooled. Are we to believe that TOBS was less standardized in 1998 than it was in 1934?

2) The US is only 2% of the Globe. Therefore, any adjustment to US data would have only a minor effect of Global data. True enough, but, if US data is so unreliable that it has had to be adjusted so much, are we to believe that world data is any better? Does anyone really think that years-old data from Asia, Africa, South America is more reliable than US data? That ocean data based on some seaman dropping a bucket overboard, hauling it back, and sticking a thermometer into it, is any better than US thermometer data?

So, unless we believe that the world temperature record is more reliable than the US record, it is likely the world record has also been similarly "adjusted". Therefore, I have discounted the GISS estimate of Global Warming by about 30%, so actual warming is about 1.0°F (0.6°C). As for allocation of this actual warming to Human- vs Natural-Causes, I believe the IPCC has over-estimated Climate Sensitivity by a factor of two or three, so I have allocated the majority of the warming 0.8°F (0.5°C) to Natural Cycles, and the remaining 0.2°F (0.1°C) to Human-Causation.

I'd appreciate comments on my estimates and conclusions. advTHANKSance

Ira

The MAGICAL Number Seven (plus or minus two)

Why do we “chunk” things in groups of about seven – seven days of the week, seven seas, seven sins, etc? The presentation I gave to the Philosophy Club in The Villages, FL, 14 March 2014 provides the theoretical answer. You may download a PowerPoint Show that should run on any Windows computer here: https://sites.google.com/site/iraclass/my-forms/PhiloMAGICALsevenMar2014.ppsx?attredirects=0&d=1

This is an easy-to-understand version of a more technical presentation I made to the Science-Technology Club in February, see: http://tvpclub.blogspot.com/2014/02/optimal-span-amazing-intersection-of.html


MILLER - PERSECUTED BY THE NUMBER SEVEN !



Way back in 1956 a classic scientific paper appeared in the Psychological Review with the intriguing title: The Magical Number Seven, Plus or Minus Two – Some Limits on Our Capacity for Processing Information. That paper was extremely important and influential and is still available online. George A. Miller begins with a strange plea:

My problem is that I have been persecuted by an integer … The persistence with which this number plagues me is far more than a random accident …

He presents the results of twenty experiments where human subjects were tested to determine what he calls our "Span of Absolute Judgment", that is, how many levels of a given stimulus we can reliably distinguish. Most of the results are in the range of five to nine, but some are as low as three or as high as fifteen. For example, our ears can distinguish five or six tones of pitch or about five levels of loudness. Our eyes can distinguish about nine different positions of a pointer in an interval. Using a vibrator placed on a person's chest, he or she can distinguish about four to seven different level of intensity, location, or duration, etc. The average Span of Absolute Judgment is 6.4 for Miller's twenty one-dimensional stimuli.

Miller also presents data for what he calls our "Span of Immediate Memory", that is, how many randomly presented items we can reliably remember. For example, we can remember about nine binary items, such as a series of "1" and "0", or about eight digits, or about six letters of the alphabet, or about five mono-syllabic words randomly selected out of a set of 1000.

At the end of his paper Miller rambles:

...And finally, what about the magical number seven? What about the seven wonders of the world, the seven seas, the seven deadly sins, the seven daughters of Atlas in the Pleiades, the seven ages of man, the seven notes of the musical scale, and the seven days of the week? What about the seven-point rating scale, the seven categories for absolute judgment, the seven objects in the span of attention, and the seven digits in the span of immediate memory?

For the present, I prefer to withhold judgment.

Perhaps there is something deep and profound behind all these sevens, something just calling out for us to discover it.
But I suspect that it is only a pernicious, Pythagorean coincidence. [my bold]

Well, it turns out that there IS something DEEP and PROFOUND behind "all these sevens" and I (Ira Glickstein) HAVE DISCOVERED IT. And, my insight applies not only to the span of human senses and memory, but also to the span of written language, management span of control, and even to the way the genetic "language of life" in RNA and DNA is organized. Furthermore, my discovery is not simply based on support from empirical evidence from many different domains, but has been mathematically derived from the basic Information Theory equation published in 1948 by Claude Shannon, and the adaptation of "Shannon Entropy" to the Intricacy of a biograph by Smith and Morowitz in 1982.


SIMPLICITY VS COMPLEXITY VS INTRICACY 


Albert Einstein wisely advises us to:

Make things as simple as possible, but no simpler!

Good advice, but how to follow it? Well, Edward Teller suggests:

Threads of simplicity … are not easily discovered in music or in science. Indeed, they usually can be discerned only with effort and training. Yet the underlying simplicity exists and once found makes new and more powerful relationships possible.

How to find those "threads of simplicity"? Well, we need to understand the difference between COMPLEXITY and INTRICACY which, in normal usage, are sometimes used interchangeably. However, there is an important distinction between them according to Smith and Morowitz (1982).

COMPLEXITY - Something is said to be complex if it has a lot of different parts, interacting in different ways. To completely describe a complex system you would have to completely describe each of the different types of parts and then describe the different ways they interact. Therefore, a measure of complexity is how long a description would be required for one person competent in that domain of knowledge to explain it to another.

A great example of UNNECESSARY COMPLEXITY is found in those "Rube Goldberg Inventions" where a relatively simple task is complicated by combining all sorts of different effects into a chain of ridiculous interactions.

INTRICACY - Something is said to be intricate if it has a lot of parts, but they may all be the same or very similar and they may interact in simple ways. To completely describe an intricate system you would only have to describe one or two or a few different parts and then describe the simple ways they interact.

A great example of useful INTRICACY is a window screen that is intricate but not at all complex. It consists of equally-spaced vertical and horizontal wires criss-crossing in a regular pattern in a frame where the spaces are small enough to exclude bugs down to some size. All you need to know is the material and diameter of the wires, the spacing betwen them, and the size of the window frame. Similarly, a field of grass is intricate but not complex.

If you think about it for a moment, it is clear that, given limited resources, they should be deployed in ways that minimize complexity to the extent possible, and maximize intricacy! That is why nearly all natural and artificial structures are configured as Hierarchical and have a common "Optimal Span".


 A SIMPLE FORMULA FOR MAXIMIZING INTRICACY AND REDUCING COMPLEXITY

What is Optimal Span?

With so many different types of hierarchical structures, each with its own purpose and use, you might think there is no common property they share other than their hierarchical nature. You might expect a particular Span of Control that is best for Management Structures in Corporations and a significantly different Span of Containment that is best in Written Language.

If you expected the Optimal Span to be significantly different for each case, you would be wrong!
According to System Science research and Information Theory, there is a single equation that may be used to determine the most beneficial Span. Thatoptimum value maximizes the effectiveness of the resources. A Management Structure should have the Span of Control that makes the best use of the number of employees available. A Written Language Structure should have the Span of Containment that makes the best use of the number of characters (or bits in the case of the Internet) available, and so on.

The simple equation for Optimal Span derived by [ Glickstein, 1996 ] is:

S_o= 1 + De
(Where D is the degree of the nodes and e is the Natural Number 2.71828459)

In the examples above, where the hierarchical structure may be described as a single-dimensional folded string where each node has two closest neighbors, the degree of the nodes is, D = 2, so the equation reduces to:

S_o= 1 + De = 1 + 2 x 2.71828459 = 6.43659

“Take home message”: OPTIMAL SPAN, S_o = ~ 6.4

Also see Quantifying Brooks Mythical Man-Month (Knol) , [Glickstein, 2003 ] and [ http://repository.tudelft.nl/assets/uuid:843020de-2248-468a-bf19-15b4447b5bce/dep_meijer_20061114.pdf] for the applicability of Optimal Span to Management Structures.

Ira Glickstein

Alzheimer’s A to Z Presentation to Philosophy Club 2/14/14 (Lee Conrad)

A person’s life is comprised of memories – that’s exactly what Alzheimer’s disease (AD) takes away from you! What makes this so difficult for caregivers: “It’s the living loss of a loved one – still with you; but, slowly drifting away”.

I presented this topic to The Villages Philosophy Club on 14 February 2014 and my Powerpoint slides are available:
https://sites.google.com/site/iraclass/my-forms/Philo%20ALZLHEIMERS%20V4.5.pps?attredirects=0&d=1

The brains of people with AD contain “Plaques” of Amyloid Beta, a protein, and tangles of another protein called tau. By the time a person manifests objectively measurable cognitive impairment, Amyloid-β, (Aß), is strongly positive in the regions of the brain known as the “DEFAULT NETWORK” – which are particularly vulnerable to (Aß) deposition.

Aß can fold into a misshapen form that causes other normal Aß molecules to assume the wrong shape and clump together. Proteins may later break off from the aggregate and seed the beginnings of the same process elsewhere. This biochemical domino effect involves a mechanism called “CORRUPTIVE PROTEIN TEMPLATING”.

As this biochemical domino effect spreads through the brain, this inexorable progression of Aß deposits engulfs most areas of the cerebral cortex (the brains outer layer) progressing to the midbrain finally reaching the lower brainstem and cerebellum in the organ’s deepest reaches.

There are no exact transition points that define when a patient has progressed from the pre-clinical phase to the mild cognitive impairment phase and then to the dementia phase.

Diagnosing dementia involves many factors which include:
o A complete physical
o A complete neurologic examination
o MRI & CT scans of the brain
o Blood & Urine tests
o Thorough discussion of patients medical history & symptoms

All of the above with a qualified neurologist and neuropsychologist

From my personal experience, observing my spouse, I could see signs of cognitive problems that needed to be addressed:
o Problems using the microwave
o Problems using TV remotes
o Problems w/ electronics that have to be programmed
o Difficulty organizing M – F pillbox for AM/PM medications
o Difficulty with driving directions through roundabouts

I obtained from a friend connected with the American Academy of Neurology suggestions for possible Neurologist from various institutions and current publications (April 2013) relevant to Mild Cognitive Impairment (MCI), Alzheimer’s dementia, genetic risk factors for late onset AD, recent genes associated with early onset AD, radioactive biomarkers for PET scans of the brain, and biochemical biomarkers present in cerebrospinal fluid (CSF). I also obtained excellent advice from a former classmate MD (specialty Internal Medicine), with experience diagnosing this disease, and his recommendation: “Seek a well- established neurologist in the field and obtain a blood test for the ApoE gene.

After a 3 - 5 hour initial exam with a neurologist associated with the Brain Institute, and the neurology dept., Shands Hospital, U. FL, Gainesville and psychologists on the staff --- the preliminary diagnosis was possible MCI with orders for a full day of neuropsychological testing, MRI of the brain and follow up in 2 months.

With the preliminary diagnosis of MCI, the review article (April 2013) on MCI was very important. The slides on MCI are self-explanatory regarding it’s clinical characterization and the significant interest in understanding the transition from MCI to AD.

After 8 hours of extensive neuropsychological testing at Shands, the final diagnosis was changed from MCI to early stages of AD, verified by the appearance of hippocampal atrophy, one of the first regions of the brain to suffer damage in AD patients. Blood tests results came back: Genotype *E2/*E4 (*E4 is a strong genetic risk factor for Alzheimer’s).

Recommended treatment: begin memory medication (Aricept) ASAP, returning in 4 months for subsequent evaluation of the effectiveness of the medication.

The blood test results for Genotype APO*E2 / APO*E4, are explained in the AAN Review Article Continuum on New Genes and Insights from old Genes, which explains the significance of APO*E4 as a genetic risk factor for AD, increasing the risk by a factor of 3 to 4 fold.

Subsequent slides explain three other genes strongly associated with “early Onset” Alzheimer’s (prior to age 65 and experiments with APP-Transgenic mice, genetically engineered (with the APP gene) to produce the precursor protein from which the human Aβ fragment is generated and spontaneously develops Aβ brain deposits at a relatively consistent age.

Future Experiments with APP-Transgenic Mice hopefully may yield important clues to solving the problem for early detection and the subsequent prevention of dementia.

Lee Conrad

Optimal Span - AMAZING Intersection of Hierarchy, Information, and Complexity Theories

I presented "Optimal Span - The AMAZING Intersection of Hierarchy Theory, Information Theory, and Complexity Theory" to The Villages Science and Technology Club today.

You may download my PowerPoint Show that should run on any Windows PC here:
https://sites.google.com/site/iraclass/my-forms/SciTechOptimalSpan10Feb2014.pps?attredirects=0&d=1

I began the presentation with Kurt Vonnegut's great poem that tells us about Tigers, Birds, and Humans and what they are compelled, by their Nature, to do. Of course: "MAN got to sit and wonder 'why, why, why?'" and then, after some study and contemplation, "MAN got to tell himself he understand!"

This Topic and my PowerPoint Show are based on my PhD dissertation: "Hierarchy Theory - Some Common Properties of Competitively-Selected Systems", System Science Department, Binghamton University, NY, 1996. If you wish to pursue further research in this area please contact me at ira@techie.com. A few copies of my dissertation are available.


The material that follows contains more detail than the PowerPoint Show.


Most complex structures are compositional or control hierarchies. An example of a compositional hierarchy is written language. A word is composed of characters. A simple sentence is composed of words. A paragraph is composed of simple sentences, and so on. An example of a control hierarchy is a management structure, where a manager controls a number of foremen or team leaders, and they, in turn, control a number of workers.


Optimal Span Hypothesis:

Optimal Span is about the same, between five and nine, for virtually all complex structures that have been competitively selected.




That includes the products of Natural Selection (Darwinian evolution) and the products of Artificial Selection (Human inventions that competed for acceptance by human society).
The hypothesis is supported by empirical data from varied domains and a derivation from Shannon’s Information Theory and Smith and Morowitz’s concept of intricacy.

What is a Hierarchy?

Hierarchy (fromGreek:ἱερός — hieros, ‘sacred’, and ἄρχω — arkho, ‘rule’) originally denoted the holy rule ranking of nine orders of angels, from God to Seraphims to Cherubims and so ondown to the Archangels and plain old Angels at the lowest level. Kind of like the organization of God’s Corporation!

The seminal book on this topic is Hierarchy Theory: The Challenge of Complex Systems[ Pattee, 1973 ]. This book includes a chapter by Nobel laureate Herbert A. Simon on “The Organization of Complex Systems”. Other chapters: James Bonner “Hierarchical Control Programs in Biological Development”; Howard H. Pattee “The Physical Basis and Origin of Hierarchical Control” and “Postscript: Unsolved Problems and Potential Applications of Hierarchy Theories”; Richard Levins “The Limits of Complexity”, and Clifford Grobstein “Hierarchical Order and Neogenesis”.
A more recent book, Complexity – The Emerging Science at the Edge of Order and Chaos, observes that the “hierarchical, building-block structure of things is as commonplace as air.” [ Waldrop, 1992 ]. Indeed, a bit of contemplation will reveal that nearly all complex structures are hierarchies.
There are two kinds of hierarchy. A few well-known examples will set the stage for more detailed examination of modern Hierarchy Theory:

Examples

1 -Management Structure (Control Hierarchy)

Workers at the lowest level are controlled by Team Leaders (or Foremen), teams are controlled by First-Level Managers who report to Second-Level managers and so on up to the Top Dog Executive. At each level, the Management Span of Control is the number of subordinates controlled by each superior.

2 -Software Package (Control Hierarchy)

Main Line computer program controls Units (or Modules, etc.) and the Units control Procedures that control Subroutines that control Lines of Code. At each level, the Span of Control is the number of lower-level software entities controlled by a higher-level entity.

3 – Written Language (Containment Hierarchy)

Characters at the lowest level are contained in Words. Words are contained in Simple Sentences. Simple Sentences in Paragraphs, and so on up to Sections, Chapters and the Entire Document. At each level, theSpan of Containment is the number of smaller entities contained by each larger.

4 – “Chinese boxes” (Containment Hierarchy)

A Large Box contains a number of Smaller Boxes which each contain Still Smaller Boxes down to the Smallest Box. At each level, the Span of Containment is the number of smaller entities contained by each larger.

Traversing a Hierarchy

Note that Examples 1 and 3 above were explained starting at the bottom of the hierarchy and traversing up to the top while Examples 2 and 4 were explained by starting at the top and traversing to the bottom.

Simple hierarchies of this type are called “tree structures” because you can traverse them entirely from the top or the bottom and cover all nodes and links between nodes.

"Folding” a “String”

A tree structure hierarchy can also be thought of an a one-dimensional “string” that is “folded” (or parsed) to create the tree structure. What does “folding” mean in this context?

As an amusing example, please imagine the Chief Executive of a Company at the head of a parade of all his or her employees. Behind the Chief Exec would be Senior Manager #1 followed by his or her First-Level Manager #1. Behind First-Level Manager #1 would be his or her employees. Behind the employees would be the First-level Manager #2 with his or her employees. After all the First-levels and their employees, Senior Manager #2 would join the parade with his or her First-Levels and their employees, and so on. If you took the long parade and called it a “string”, you could “fold” it at each group of employees, then again at each group of First-Level Managers, and again at the group of Senior Managers, and get the familiar management tree structure!

The above “parade” was described with the Chief Exec at the head of it, but you could just as well turn it around and have the lowest-level employees lead and the Chief Exec at the rear. When military hierarchies go to war, the lowest-level soldiers are usually at the front and the highest-level Generals well behind.

A more practical example is the text you are reading right now! It was transmitted over the Internet as a string of “bits” – “1″ and “0″ symbols. Each group of eight bits denotes a particular character. Some of the characters are the familiar numbers and upper and lower-case letters of our alphabet and others are special characters, such as the space that demarks a word (and is counted as a character that belongs to the word), punctuation characters such as a period or comma or question mark, and special control characters that denote things like new paragraph and so on.

You could say the string of 1′s and 0′s is folded every eight bits to form a Character. The string is folded again at each Space Character to form Words. Each group of Words is folded yet again at each comma or period symbol that denotes a Simple Sentence. Each group of Simple Sentences is again folded to make Paragraphs, and so on.

You could lay out a written document as a tree structure, similar to a Management hierarchy. The Characters would be at the bottom, the Words at the next level up, the Simple Sentences next, the Paragraphs next, and so on up to the whole Section, Chapter, and Book.

What is Optimal Span?

With all these different types of hierarchical structures, each with its own purpose and use, you might think there is no common property they share other than their hierarchical nature. You might expect a particular Span of Control that is best for Management Structures in Corporations and a significantly different Span of Containment that is best in Written Language.

If you expected the Optimal Span to be significantly different for each case, you would be wrong!
According to System Science research and Information Theory, there is a single equation that may be used to determine the most beneficial Span. Thatoptimum value maximizes the effectiveness of the resources. A Management Structure should have the Span of Control that makes the best use of the number of employees available. A Written Language Structure should have the Span of Containment that makes the best use of the number of characters (or bits in the case of the Internet) available, and so on.

The simple equation for Optimal Span derived by [ Glickstein, 1996 ] is:

S_o= 1 + De
(Where D is the degree of the nodes and e is the Natural Number 2.71828459)

In the examples above, where the hierarchical structure may be described as a single-dimensional folded string where each node has two closest neighbors, the degree of the nodes is, D = 2, so the equation reduces to:

S_o= 1 + De = 1 + 2 x 2.71828459 = 6.43659

“Take home message”: OPTIMAL SPAN, S_o = ~ 6.4

Also see Quantifying Brooks Mythical Man-Month (Knol) , [Glickstein, 2003 ] and [ Meijer, 2006 ] for the applicability of Optimal Span to Management Structures.

[Added 4 April 2013: The Meijer, 2006 link no longer works. His .pdf document is available at http://repository.tudelft.nl/assets/uuid:843020de-2248-468a-bf19-15b4447b5bce/dep_meijer_20061114.pdf ]

Examples of Competitively-Selected Optimal Span

Management Span of Control

Management experts have long recommended that Management Span of Control be in the range of five or six for employees whose work requires considerable interaction. Depending upon the level of interaction, experts recommend up to nine employees per department.This recommendation comes from experience with organizations with different Spans of Control. The most successful tend to have Spans in the recommended range, five to nine,an example of competitive-selection.

When the lowest level consists of service-type employees, whose interaction with each other is less complex, there may be a dozen or two or more in a department, but there will usually be one or more foremen or team leaders to reduce the effective Management Span of Control to the range five to nine.Corporate hierarchies usually haveabout the same range of first-level departments reporting to the next level up and so on.

Say you had a budget for 49 employees and had to organize them to make most effective use of your human resources. Which of the following seems most reasonable?

(A) you have ONE manager and 48 workers, which is a BROAD hierarchy. Management experts would say a Management Span of Control of 48 is way too much for anyone to handle!

(B) you have a third-level chief executive, three executive-level managers, each with three department managers, totaling THIRTEEN managers in a three-level management hierarchy and only 36 workers, which is a TALL hierarchy with an average Management Span of Control of only 3.3. Management experts would say this is way too inefficient with too many managers!

(C) you have a second-level manager and six department managers, totaling SEVEN managers and 42 workers in a MODERATE hierarchy with an average Management Span of Control of about 6.5. Management experts would say this is about right for most organizations where the workers have to interact with each other. Optimal Span theory supports this common-sense belief!

Human Span of Absolute Judgement

Evolution and Natural Selection have produced the human brain and nervous system and our senses of vision, hearing, and taste. It turns out that these senses are generally limited to five to nine gradations that can be reliably distinguished. It is also the case that we can remember about five to nine chunks of information at any one time. This is another example of competitive-selection, where, over the eons of evolutionary development, biological organisms competed and those that best fit the environment were selected to survive and reproduce.



George A Miller wrote a classic paper titled The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information [ Miller, 1956 ]. He showed that human senses of sight, hearing, and taste were generally limited to five to nine gradations that could be reliably distinguished. Miller’s paper begins as follows:



"My problem is that I have been persecuted by an integer [7 +/- 2]. For seven years this number has followed me around, has intruded in my most private data, and has assaulted me from the pages of our most public journals. This number assumes a variety of disguises, being sometimes a little larger and sometimes a little smaller than usual, but never changing so much as to be unrecognizable. The persistence with which this number plagues me is far more than a random accident. There is, to quote a famous senator, a design behind it, some pattern governing its appearances. Either there really is something unusual about the number or else I am suffering from delusions of persecution.Miller’s paper is well worth reading and is available on the Internet at this link [Miller, 1956]"

Glickstein’s Theory of Optimal Span

Miller’s number also pursued me (Ira Glickstein) until I caught it and showed, as part of my PhD research,[ Glickstein, 1996 ]that, based on empirical data from varied domains, the optimal span for virtually all hierarchical structures falls into Miller’s range, five to nine. Using Shannon’s information theory, I also showed that maximum intricacy is obtained when the Span for single-dimensional structures is S_o = 1 + 2e = 6.4 (where e is the natural number, 2.71828459). My “magical number” is not the integer 7, but 6.4, a more precise rendition of Miller’s number!

Hierarchy and Complexity

Howard H. Pattee, one of the early researchers in hierarchy theory, posed a serious challenge:

Is it possible to have a simple theory of very complex, evolving systems? Can we hope to find common, essential properties of hierarchical organizations that we can usefully apply to the design and management of our growing biological, social, and technological organizations? [Pattee, 1973]

Pattee was the Chairman of my PhD Committee and I took the challenge very seriously!

The hypothesis at the heart of my PhD dissertation is that the optimal span is about the same for virtually all complex structures that have been competitively selected. That includes the products of Natural Selection (Darwinian evolution) and the products of Artificial Selection (Human inventions that competed for acceptance by human society).

Weak Statement of Hypothesis

In  the “weak” statement of the hypothesis, it is scientifically plausable to believe that diverse structures tend to have spans in the range of five to nine, based on empirical data from six domains plus a computer simulation.

The domains are:

Human Cognition: Span of Absolute Judgement (one, two and three dimensions), Span of Immediate Memory, Categorical hierarchies and the fine structure of the brain. These all conform to the hypothesis.

Written Language: Pictographic, Logographic, Logo-Syllabic, Semi-alphabetic, and Alphabetic writing. Hierarchically-folded linear structures in written languages, including English, Chinese, and Japanese writing. These all conform to the hypothesis.

Organization and Management of Human Groups: Management span of control in business and industrial organizations, military, and church hierarchies. These all conform to the hypothesis.

Animal and Plant Organization and Structure: Primates, schooling fish, eusocial insects (bees, ants), plants. These all conform to the hypothesis.

Structure and Organization of Cells and Genes: Prokaryotic and eukaryotic cells, gene regulation hierarchies. These all conform to the hypothesis.

RNA and DNA: Structure of nucleic acids. These all conform to the hypothesis.

Computer Simulations: Hierarchical generation of initial conditions for Conway’s Game of Life. (Two-dimensional ). These all conform to the hypothesis.

Strong Statement of Hypothesis

Shannon’s information theory, andthe concept of intricacy of a graphical representation of a structure [ Smith and Morowitz, 1982 ] can be used to derive a formula for the optimal span of a hierarchical graph.

This work extended the single-dimensional span concepts of management theory and Miller’s “seven plus or minus two” concepts to a general equation for any number of dimensions. I derived an equation that yields Optimal Span for a structure with one-, two-, three- or any number of dimensions!

The equation for Span (optimal) is:

S_o= 1 + De

(Where D is the degree of the nodes and e is the Natural Number 2.71828459)

NOTE: For a one-dimensional structure, such as a management hierarchy or the span of absolute judgement for a single-dimensional visual, taste or sound, the degree of the nodes, D = 2 . This is because each node is a link in a one-dimensional chain or string and so each node has two closest neighbors.

For a two-dimensional structure, such as a 2D visual or the pitch and intensity of a sound or a mixture of salt and sugar, D = 4. Each node is a link in a 2D mesh and so each node has four closest neighbors.

For a 3D structure, D = 6 because each node is a link in a 3D egg crate and has six closest neighbors.

Some of the examples in Miller’s paper were 2D and 3D and his published data agreed with the results ofthe formula. The computer simulation was 2D and also conformed well to the hypothesis.

In normal usage, complexity and intricacy are sometimes used interchangeably. However, there is an important distinction between them according to [ Smith and Morowitz, 1982 ].

COMPLEXITY - Something is said to be complex if it has a lot of different parts, interacting in different ways. To completely describe a complex system you would have to completely describe each of the different types of parts and then describe the different ways they interact. Therefore, a measure of complexity is how long a description would be required for one person competent in that domain of knowledge to explain it to another.

INTRICACY - Something is said to be intricate if it has a lot of parts, but they may all be the same or very similar and they may interact in simple ways. To completely describe an intricate system you would only have to describe one or two or a few different parts and then describe the simple ways they interact. For example, a window screen is intricate but not at all complex. It consists of equally-spaced vertical and horizontal wires criss-crossing in a regular pattern in a frame where the spaces are small enough to exclude bugs down to some size. All you need to know is the material and diameter of the wires, the spacing betwen them, and the size of the window frame. Similarly, a field of grass is intricate but not complex.

If you think about it for a moment, it is clear that, given limited resources, they should be deployed in ways that minimize complexity to the extent possible, and maximize intricacy!

Using [ Smith and Morowitz, 1982 ] concepts of inticacy, it is possible to compute the theoretical efficiency and effectiveness of a hierarchical structure. If it had the Optimal Span, it is 100% efficient, meaning that it attains 100% of the theoretical intricacy given the resources used.If not, the percentage of efficiency can be computed. For example, a one-dimensional tree structure hierarchy is 100% efficient (maximum theoretical intricacy) with a Span of 6.4. For a Span of five, it is 94% efficient (94% of maximum theoretical intricacy).It is also 94% efficient with a Span of nine. For a Span of four or twelve, it is 80% efficient.

Ira Glickstein