Archive for June, 2011

Date: June 30th, 2011
Cate: Finance, Risk & Stability, Systems

Systems thinking for finance geeks: Graphorisms

thanks to @semanticwill for the title help.

I created a quick and purposely reduced set of graphics for explaining systems states and actors. These are called graphorisms like an active character based language. Each Graphorism provides a way of framing or discussing in the abstract a system state or condition. The presentation explains these for finance. The presentation may not make much sense without context.

Systems thinking is all about capacity, tension, evolution, risk, tight coupling and of course linkages. It is basically a series of verbs and forms for expressing state. The end user then supplies domain specific nouns. The idea to have a shared language for expressing opinion about system state. The language is purposely crude and abstract. This is done so that people feel comfortable drawing things for themselves and that groups hopefully don’t converge on false assumptions about state completeness or exactitude in whole state awareness or future potentials. If interested in the powerpoint, write to me nick at The graphics are under CC rights of attribution so use at will and share alike.

Please note I have left out the typical stock flow stuff that tends to overwhelm the communication value of typical systems thinking diagrams for many people.

Date: June 22nd, 2011
Cate: Finance, Risk & Stability, Uncategorized
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Banking & Insurance critical instability by design

I am wrapping up my book “The Long Game: Mental models for better investing and capital allocation”.  Along the way I am finishing some graphics which highlight the processes and systems that lead to success and failure.  Capitalism isn’t good or bad, it is a powerful process that is harnessed in different ways.

Understanding who survives and thrives in capitalism means understanding process. The Long Game looks at competition, investing and capital allocation systems through the eyes of a systems thinking approach. Most of the models and analogies are borrowed from biology, ecology and psychology to express why some win, some lose and it always keeps changing.  Here are some graphics I am building.  Let me know if you have any critiques on how to show the story in a more simple or engaging way.

Oh and if there are any publishers out there…I really need a great editor.

Date: June 17th, 2011
Cate: Risk & Stability, Systems
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The Black Swan of Cairo: The law of conservation of risk

I don’t agree with everything Taleb proposes, but his last two papers have been very interesting. Here is a link to the Journal of Foreign Affairs paper: The Black Swan of Cairo If one assumes what I would call conservation of risk or entropy then the argument Taleb seems to make in the paper is that by covering up a political/economic system with oppression to “protect” visible stability, all one is doing is masking underlying instability.  Eventually the “stable” system will become fragile at the surface or visible layer.

My own interpretation is that there is a “conservation” of entropic variance in any system and that if one dampens a variable long enough, the system will either collapse or overwhelm the dampening mechanism due to the system’s entropic reserves which are stored somewhere else in the system state.

A shorter version of this idea is a “conservation of risk law” similar but not a total analog to the physics principals of conservation of mass, matter and energy.  The idea being that a risk (potential undesired or unforseen system state) doesn’t disappear, it is merely transferred or suppressed in the present measured state only to manifest itself in the future. The risk entropy of system doesn’t disappear.

CDO’s, bad accounting, debt obfuscation and faith in co-variance based asset diversification are financial examples of this.  In systems engineering it is over-engineering the complexity of the safety mechanisms to such a degree that they induce accidents or failures greater than what they were designed to mitigate.

Date: June 17th, 2011
Cate: Risk & Stability, Systems
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Systems and Antifragility paper from Taleb

Nassim Taleb’s new paper: A Map and Simple Heuristic to Detect Fragility, Antifragility, and Model Error

This is a fun paper which I like a lot as it implicitly tries to get at stability of assumptions and process by ignoring the flaws in assumptions about primary statistical moments used in most stochastic models (i.e. the moments being fixed or measured correctly). True confession, I am interested in intrinsic value not price, but like the paper for what it says about most systems which are by definition complex and wrongly modelled and managed using Gaussian tools and mis-measurements.

One of my favorite parts in the paper is the concept of a random process not equating to the square of its outcomes. :

“For example, take a conventional die (six sides) and consider a payoff equal to the number it lands on. The expected (average) payoff is = 3.5 . Now consider that we get the squared payoff,= 12.25, so, since squaring is a convex
function, the average of a square payoff is higher than the square of the average payoff.”

This struck me as interesting becuase squaring something reflects a proxy of a growth process (albiet) with a single step. Many growth/decay (exponential) processes such as companies or sectors experience are by definition exponential relative to their underlying fundamental drivers. The example is a simple way of showing the miss-fit of a simple stochastic metric to a growth dynamic. It doesn’t even begin to touch on the failure of most models to consider rational boundaries or limits to growth which may be related to fundamental dynamics feeding the process.

Another unquestioned component is that the models are all looking at and using price which is purely an abstracted guess at value being made by traders who don’t understand intrinsic value.  My argument is that price is the outcome of two opinions shared at a point in time and both of them are wrong 99% of the time. If the arbiters of price were correct they could beat the market.

The paper is one of Taleb’s more interesting and presents a few concepts as words which could be papers in thier own right: Philostochaticity: Biological, Economic, and Political Systems Starved of Variations and the relationship between convexity and anti-fragility, which some deem to be robustness.

There is a lot in the paper and it is definitely worth more than one read.  I am less interested in this for its “price” arguments as I care about intrinsic value, but am more interested in what it has to say about understanding systems (all of which are complex) with out-moded variants on guassian tools and assumptions.  Garch just doesn’t work so hot in the real connected world convex world.

Date: June 3rd, 2011
Cate: Systems, Uncategorized
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Thinking in biological systems with help from Ed

My brilliant polymath friend Ed Rietman sent me a fun paper to read the other day.  He used to head up our Nanotechnology and optics research at Starlab. He works on cancer research, phononic crystals and a host of other deeply interesting fields.  He is working on a bench set-up in his basement that will test a low energy desalinization method using acoustic waves as well as an optical analog to the event horizon of a black hole.  Pretty cool stuff, I always learn a lot listening to Ed.

Tree of eukaryota

Learning new mental models of how things works allows one to synthesize models as analogs for other areas.  Combining investing, finance, risk, behavior, science and biological processes into functional tools and ways of thinking can be very powerful. My book project (The Long Game: mental models for better investing and capital allocation) uses biological, ecological, statistical and social processes to explain how to invest and allocate capital.

Ed sent me a cool paper the other day which I read on the train for fun.  It is titled: Cancer tumors as Metazoa 1.0: tapping genes of ancient ancestors. The general idea is that cancer is a process derived from older cellular functions that exist inactive in the genome and only being expressed under environmental stresses.  The cool idea is that legacy genomic functions are carried forward and cause cancer.  The reason this is so interesting as a field of research is that it would mean that cancer has a finite set of expression types instead of unlimited types. One of the interesting points in the paper is that most plants don’t get cancer and how tumors can often express themselves with hundreds of cell types.

The paper is thankfully simply written so a hobbyist like me can follow along. The mental model of an evolved system still retaining legacy functions which may express themselves detrimentally to the organism is quite interesting.  It is a trade off between functional learning, reproductive progress, climbing a complexity fitness hill and embedded risk. Collecting and synthesizing models of how things work is a hobby of mine, with a friend like Ed, you never know what you can learn that lets you think about the world in a whole new way.