Wednesday, January 30, 2013

New Facts About The State Pension Funds



Morningstar recently did a review of the US State Pension Funds for their investors (here and in the video above). The review found that the Wisconsin Retirement System (WRS) was the strongest fund in the country with a funding ratio of 99.8%. This means that Wisconsin's unfunded liability per resident is $23, the lowest of any public pension fund in the US. The results beg a lot of questions: How is Wisconsin able to have a solvent Public Pension Plan? Why can't the other States?

Before answering these questions, there is a simple takeaway from these findings: States can afford to offer solvent Pension Funds for their employees. What is more, States could offer solvent Pension Plans to all their citizens. And, corporations could also offer solvent Pension Plans to their employees. The Right Wing simply does not want to provide pensions (the same Debt Alarmists that would think $23 is too large an unfunded liability) and any other form of social security to workers.


Watch Henry Ford Preview on PBS. See more from American Experience.
If you want some insight into why the Right Wing opposes solvent Pension Plans (Public or Private) watch the PBS American Experience documentary on Henry Ford (here). Ford is one of the Titans of Industry and had opinions befitting his class: anti-union, anti-worker, anti-Semetic, etc. The Ford River Rouge Complex was run like a police state, a model for Right-Wing US industry. The Ford Pension Plan was the result of UAW labor disputes and bargaining, not enlightened, financially adept management.

Why are the State Pension Plans in trouble? Two reasons: The Subprime Mortgage Crisis and years of underfunding or outright stealing from Pension Plans by politicians. The goal of all this is to wake up one day during a financial crisis and claim that the State can no longer pay its pensions even as the real reason is years of deliberate fund weakening. Why is Wisconsin able to provide its employees a solvent Public Pension Plan? By law, politicians cannot touch the WRS. It's that simple. One more thing, it's not because the Public Sector cannot afford to provide pensions. Wisconsin currently has a $484 million surplus (here) none of which is needed to provide continued Pension Plan support.

Thursday, January 17, 2013

Software Development Recap 2012

I have been working on a number of statistical packages written in the public domain R programming language. Last year I put two packages together. A package is a collection of R functions, usually built on other R packages, that implement a statistical procedure. The two packages were:
  • hlmmc In the late summer of 2012, I taught a graduate-level class in Hierarchical Linear Models (HLMs, sometimes called Multilevel Models) at the University of Tennessee (UT-Knoxville). The hlmmc package was developed for the course to facilitate Monte Carlo studies (computer-intensive methods for the numerical analysis of messy problems) and teaching HLMs. More information about hlmmc (also a public domain package) is available here. I am also serializing my course lectures (here). The lectures were essentially demonstrations of the hlmmc package.
  • ws This package allows state-space time series models I have developed to be simulated, re-estimated or extended. I have used the ws package to develop models of the world system (WL20), the US economy (USL20), other countries in the world system and various forecasting models (see examples of the forecasting models here and the US Stock Market models here). More information about the ws package is available here.
I use another set of routines to develop the models made available with the ws package. These routines  select the best model using the AIC criterion from a number of models starting with a Random Walk as the null hypothesis.  I believe that the routines I am currently using could be simplified for general use, but there is still some testing that needs to be done to make sure the simplified functions generate similar "best" models. For 2013, I should be able to complete the rw package, depending on how the testing goes.

As I developed this software, I realized that the I needed another outlet to develop the theory and rationale underlying the programs. In 2012, I created a number of other blogs for this purpose. As time permits in 2013, I will post results to these blogs:
NOTE: My major area of interest involves state space models and the application of systems theory to statistics, computer simulation and societal development. I have been working in this area since the late 1970's (here). For many years I had used my own software package to do state-space analysis. It was a collection of Fortran subroutines (see John Nash's book Compact Numerical Methods for Computers plus other routines from other sources) driven by an interpretedstack-based RPN (Reverse Polish Notation) language similar to Postscript and also written in Fortran (based on A. Colin Day's Book Fortran Techniques: With Special Reference to Non-numerical Applications). When R became available in the late 1990's, I started writing the routines I am currently using in the R language

Wednesday, January 16, 2013

That's Incredible, Steve!



I just finished reading Walter Isaacson's book Steve Jobs. I had never seen the early Pixar animated films that were done when Jobs took over the company after being fired from Apple. We have been ordering DVDs through Netflix and last night we saw The Incredibles (2004) the sixth film produced by Pixar Animation Studies. Here's my review.

My interest in animation comes from my interest in the computer programs and systems (the RenderMan language and the Reyes rendering architecture, in this case) that were used to create the movie. I'll go into more detail in later posts, but the Incredibles advanced over early animations because there was an all-human cast that required new technology to animate detailed human anatomy, clothing, hair and skin. Skin is a particular problem since it is translucent and required the development of a new technique called subsurface scattering.

The plot of the movie involves the trials of a superhero family who have been essentially put into a witness protection program to hide their powers. The video above (which I think is an out-take since I don't remember it from the movie) shows some of the powers of baby Jack-Jack who is left with a teenage baby sitter (being interviewed by Agent Rick Dicker, who reminded me a little of Richard Nixon in the clip above) while the rest of the family is off fighting the movie's villain, Syndrome. In the end, Jack-Jack does the job for them and the clip above shows some of the powers he used.

The movie starts out a little slowly but is very, very funny. My wife and I both loved it. That's Incredible, Steve!

Tuesday, January 15, 2013

Causal Model Recap for 2012

This blog is partly about developing causal models to understand the world around us as it is happening in real time (the other part is simply about me venting steam while I read the popular press). It is based on reading Judea Pearl's book Causality: Models, Reasoning and Inference.  Prof. Pearl is a computer scientists studying artificial intelligence by asking how we can formalize for machine learning what humans do so easily: establish causal connections between events. Prof. Pearl's answer to the question is through the use of directed graphs.

One way to test Prof. Pearl's ideas is to look at current events and try to clarify arguments by developing causal models. Looking back on a year of doing this kind of testing here are the models, brief descriptions of the arguments and a link to the original blog postings.


Arguably, the most important event in 2012 was the unfolding Financial Crisis that started in 2007. A model I developed in January of 2012 (here) looked at the role of inequality (inequality has been increasing the US during the period of Neoliberalism to levels not seen since the Great Depression in the 1930--see the graph here) and the effect that a Wealth Tax might have on economic growth. The model indicates that inequality has a role in decreasing economic growth and that a wealth tax would have the opposite effect. The model is meant to confront the right-wing argument that inequality is necessary for economic growth.


I developed another model on a similar topic in February (here). In this model (click to enlarge) I pointed out that right-wing seems to make its arguments by reversing the actual direction of causation. The argument is that the Entitlement Society is creating economic problems while the direction of causation is in the other direction: economic forces (financial crises and globalization) are creating the need for increased Federal benefit spending. More data (and more models) will be needed to determine whether reverse-causation is right-wing strategy or simply confusion over the direction of arrows in positive feedback loops (viz., warm temperatures causing people to emit more CO2).

In May I picked up the topic of climate change (here) looking at a recent argument being made by the Climate Denial crowd, in this case Richard Lindzen. He was arguing that global temperature increase resulting from CO2 emissions (notice that the role of CO2 emissions in climate change has been admitted here) would trigger a negative feedback effect that would keep global temperature under control.

The negative feedback control loop he hypothesized involved the reduction of Cirrus Clouds. Since Cirrus Clouds are thought to play a role in creating the greenhouse effect (increasing global temperature) any reduction would act to control temperature.

This is an interesting and sophisticated argument from the Climate Change Deniers. There are many poorly understood feedback loops in the global climate system (some are reviewed here) and Cirrus Cloud formation is certainly one of them. In this case, there is little data that supports the argument.

The world climate system is obviously complex and some parts (local weather) are probably chaotic (more here). For us non-climate scientists, our best hope is to be able to develop the arguments as causal models and watch as the data accumulates.


In November (here) after Hurricane Sandy I looked at the role of climate change in severe weather formation and its consequences. The causal model above shows that the difference between air and sea temperature caused by climate change, in addition to increase atmospheric water vapor, will increase the intensity and consequences of hurricane flooding.


In a later post the same month (here) I added economic causes to the model showing how economic growth creates not only increased CO2 emissions but also increased coastal development. With greater coastal development and greater CO2 emissions, damage from Hurricanes can be expected to increase even more.

I would like to promise that I could develop causal models for the entire climate system at some point this year.  That would be really useful but also pretty premature. It will be along time before enough data is available to use in critiquing the models. On the other hand, the IPCC is scheduled to finalize the Fifth Assessment Report (AR5) in 2014. It would be useful to have a collection of models ready to use in reading AR5. And of course, the Subprime Mortgage Crisis is winding down and will continue to provide opportunities for casual modeling as various political parties and commentators try to put the monkey on someone else's back for the event.

Saturday, January 12, 2013

The Simple Mathematics of Gun Violence


A recent article in the NY Times about the rapid increase in gun sales after President Obama's re-election (here) got me thinking about the impact of a more heavily armed US population. An easy way to express the idea mathematically is with an I=PAT identity.

The I=PAT identity is a general formula for determining the impact of a human activity. In the formula, I = Human Impact, P = population, A = affluence and T= technology. Typically, affluence is measured by GDP (Gross Domestic Product) per capita and technology is some intensity measure such as energy intensity or emission intensity. For example, the Kaya Identity is written as:

CO2 Emissions = Population (GDP/Population) (Energy/GDP) (CO2 Emissions/Energy)

where Technology = (Energy/GDP)(CO2 Emissions/Energy) = (Energy Intensity)(Emission Intensity). An important feature of ImPAcT models is that they are true by definition, that is, they are identities. If you know the ratios (sometimes called intensive variables) and the ratios are relatively stable, you can, for example, predict the impact of population growth on CO2 emissions fairly accurately, at least for a few years into the future. Another way to say this is that without decreasing energy intensity or emission intensity, population growth will increase CO2 emissions.

So, let's apply this thinking to the increases in gun ownership that are happening right now in the US.

(Mass Murder) = Population (Guns/Population) (Lunatics/Gun) (Mass Murders/Lunatic)

here the affluence measure is (Guns/Population) and the Technology measure involves the technology of mass murder  (Lunatics/Gun) (Mass Murders/Lunatic) and captures increased gun ownership and the question of whether the mentally ill are more prone to violence. From this equation, we can predict that mass murders will increase in the US as gun ownership increases, other things being equal.

Now, the NRA argues (here) that mass murders would be prevented by wider gun ownership. That can be added to the equation:


(Mass Murder Prevented) = Population (Guns/Population) (Lunatics/Gun) (Mass Murders/Lunatic) (Mass Murder Prevented/Mass Murder)


Here the technology is (Lunatics/Gun) (Mass Murders/Lunatic) (Mass Murder Prevented/Mass Murder) which hinges on how many mass murders were prevented by armed citizens on the scene. There were armed citizens on the scene when Rep-Gabriele Giffords was shot in Tuscon, Arizona (here).  They did not draw their weapons and take out the shooter, Jared Lee Loughner

There are multiple reasons why in a concealed-carry State such as Arizona armed citizens did not intervene. First, if you pull your concealed weapon when you suspect mass murder is about to take place, you stand the risk of being mistaken for the shooter and taken out by some other concealed-carry citizen or by law enforcement. Second, in the chaos of an unfolding mass murder, you may not be sure you are targeting the right person as actually happened here. At the end of the day, an armed citizen at the site of a mass murder will not pull their weapon and will not prevent a mass murder for fear of either being killed themselves or killing the wrong person. 

Therefore, (Mass Murder Prevented/Mass Murder) = 0 and increases in armed population will increase the number of mass murders. This suggests that the only way to reduce mass murder is to reduce the number of lunatics (increased spending on mental health), reduce lunatic's access to weapons (background checks) or reduce gun ownership.  The political debate is just beginning.

NOTE: The IPCC has made extensive use of I=PAT models in climate change reports, another area that needs more simple, clear mathematical thinking.

Thursday, January 10, 2013

Back Door Slam: Gotta Leave


I just heard this cut on Pandora's Blues Channel from the short-lived UK group Back Door Slam (the group broke up in 2009). The sound of  Davy Knowles' guitar caught my ear (you can hear a little better version of "Gotta Leave" here).  Here are the lyrics from Lyric Wiki and a quick guess at the changes:

Ask me no questions, when I try to tell you
My best intentions, were always with you
And don't raise your voice, just try to understand
Save your loving, for some other man

Oh, you must have gotten me confused, with some other man
Who will lay you down, on the line for you
Tell me how, can I trust you, when you can't trust yourself
I've gotta leave you baby, find myself, somebody else
Ooooooh...

Thought we were over it, hmmm, thought we'd passed the worst
It was so always hard to tell, if it were a blessing or a curse
Sometimes it was so sweet, in between a lying
Every time we spent laughing, there was two times I spent crying

Oh, you must have gotten me confused, with some other man
Who will lay you down, on the line for you
Tell me how, can I trust you, when you can't trust yourself
I've gotta leave you baby, find myself, somebody else
Ooooooh...

Can't keep hiding...
No no you can't keep lying...

Oh, you must have gotten me confused, with some other man
Who will lay you down, on the line for you
Tell me how, can I trust you, when you can't trust yourself
I've gotta leave you baby, find myself, somebody else
Ooooooh...

To the changes:

Bm7, D7, A7, Em7, F#m7, Bm7
(bridge)Em7, Bm7, Em7, F major7