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Cause Effect and Dr Kaplan's Risk Matrix
PROBABILITY CLOUDS, RISK REWARD, MORENO RISK MATRIX AND PERFORMANCE MEASUREMENT
Phil Jones wrote:
I was thinking over your statement regarding Dr Kaplans Risk Matrix, I had some thoughts,
CAUSE EFFECT PRESUPPOSITIONS
In NLP as you know, there's the idea of cause effect presuppositions.
This is that X causes y and then a statement can be made that
presupposes this either implicitly or explicitly.
PHYSICS MODELS OF PROBABILITY CLOUDS
In physics in the photoelectric effect, if you shoot a photon
at an atom, according to the cause effect presupposition description,
the photon would cause an electron to jump energy levels, thus if
aggregated, this would create an electric current.
However the physics models don't allow for an electron to be in
a certain place around the nucleus for the photon or groups of
photons to hit the electron in a cause effect presupposition manner
for the electron to shift energy levels.
The physics models only at this stage allow for the electron to
be in a certain orbit or energy state around the nucleus in a balance
of probabilities. The electron orbits in the physics models are
described as clouds of probabilities where the probability is high
that they will be at a certain place in the cloud. The premise that
they will be in a certain place at a certain time isn't certain,
according to the physics models.
So according to your above statement, if you have an organisation, there
could be cause effect statements that describe that certain things
cause risks but it isn't certain that that certain causes caused the
BALANCE OF PROBABILITIES
More precisely would be that certain things, according to a balance
of probabilities, cause risks and this could be described in a
cause effect presupposed sentence. However for a specific event
that tries to specifically quantify that x created y risk then
that wouldn't be possible according to an analogy to the physics
In the Supreme Court guide that I have, it mentions that in the
Rules of Evidence there is a standard of proof based on a "balance
of probabilities". In a civil lawsuit there might be imperfect
information or information that is unquantifiable. This overlaps
with the field of study of epistemology, which NLP is partly based
EBS - UNDERSTANDING RISK VERSUS MANAGING RISK
In the Heriot Watt EBS module on Strategic Risk Management they
outline that understanding risk is different from managing risk.
MORENO RISK MATRIX FOR UNDERSTANDING/MANAGING RISK
So to build a two dimensional matrix similar to Dr Kaplan's in
his position paper,
type | randomness | balance of probabilities
risk | monte carlo | probability clouds, pattern matching
managing | risk management systems | discretionary decision making
risk | pricing copulas
Some hedge fund managers manage risk using internal decision strategies
or discretionary techniques that are based on probabilities and pattern
matching. I recently saw a piece of software in Equis corp's catalogue
that uses probability calculus for example.
However I saw on the medical TV show House the MD's debating on whether
there existed cognitive pattern matching. They concluded that it didnt
exist. However computers can do that using image recognition mathematics.
I discussed image recognition with Industry Canada a few years ago.
Some mathematicians try to understand risk using random monte carlo
simulations. My understanding is that these mathematicians usually aren't
as strong in discretionary trading however I may be mistaken as I don't
have enough data to support this.
I happened to meet a former professional chess player who traded options
and he mentioned he knew the mathematics it was based on. Dr Schiro
mentioned that in calculus, it's useful to use educated
guesses to derive solutions. However this approach uses probabilities
and not monte carlo simulations. I'm not sure if it's possible to
play chess or solve calculus equations using monte carlo simulations.
In the financial crisis, there was/is a disconnect in that many
hedge fund managers relied on risk management systems to trade when
they should have combined using discretionary techniques. One example
is the use of trading systems based on pricing copulas that broke down
during the financial crisis.
[According to Institutional Investor magazine in 2011 many hedge funds
are below their high water mark, which means they won't be earning
performance fees for this year, this shows correlation which sort of
corresponds to the cause effect presuppositions - in this case it might
be that many hedge funds invested by momentum investing, which means
in a sideways or bear market their performance decreases.]
On the opposite note, some hedge fund managers used purely discretionary
techniques to trade. Case in point is the recent failure of a certain
investment bank within the last 2 months that failed even though Basel
2 or 3 was in effect. The firm took on too much risk on a sovereign debt
trade that was discretionary.
Dr Kaplan wrote in his "dimensions paper" that one of the ways to
deal with unknown risks is through hedging which can be a discretionary
or non discretionary endeavour.
THE HUMAN ELEMENT TO RISK/REWARD
So, risk/reward management systems should probably have some element of
human based real time discretion in managing risk. Dr Kaplan, in his
letter to the FT, wrote that the rebalancing accounting used by
financial engineers in calculating the risk reward profile of
investment positions should be taught in business schools.
Maybe this could lead to new job descriptions as computers are
often seen as tools to reduce the level and numbers of human input
into decision making, which is probably a mistaken proposition.
RISK/REWARD QUANTIFICATION IN IMPERFECT SITUATIONS
However the constraint is how to quantify the discretionary
risk/reward management decisions of employees in unquantifiable
situations where there is imperfect information or the results are
not apparent, either immediately or in the future.
One way that was used in the past is to use track records and
control charts. However there are constraints to this approach.
There are probably other methods.
Another way is social proof, which is used on the Internet.
Humans have a sense of what creates risk and reward, at least
that's the sense I have of the people I have met and seen.
But there is a lag time.
I was discussing with a friend that certain fables have a quality
to them. The opposite of risk management is quality I think.
In control charts, computers probably couldn't quantify results
properly. There is a new push for search engines to search the deep
web, not just metadata and web pages.
SHIFT FROM FINANCIAL REPORTING TO FINANCIAL DECISIONS
In the Heriot Watt EBS module on Financial Risk Management
it stated that at one time financial reporting was valued highest
however that had shifted to financial decision making. Whether
that will be the same during/after the financial crisis remains
to be seen.
So maybe there will be a shift back to financial reporting or at
least hybrid methods. Dr Schiro mentioned that some financial analysts
in his former firm could "eyeball" financial statements to find
the relationships between numbers. This is a form of discretionary
decision making. I can do this also using financial statements of
my trading records. I adjusted my trading according to the results
According to Dr Bandler if one person can have an ability, it can be
taught and replicated. That's the hope at least.
One of the things I developed was a way to target synapse generation
in certain areas of the brain. For instance, for the ability to eyeball
charts, it's possible to develop synapses in the visual cortex, the back
of the head, so that any cognition can be improved and any deficits
of synapses can be remedied.
One of the reasons I wrote this letter is because I have a model of
self diagnostics, which I think is a key part to risk/reward. Self
diagnostics is invaluable in medical situations. My doctor mentioned
that many doctors can't self diagnose. This model can provide insight
in unquantifiable situations and uses a balance of probabilities
approach also. I hope to write on it soon.
IMPLICATIONS FOR PERFORMANCE MEASUREMENT
Risk management models development will help to reduce systemic
risk and provide a structure for new job categories so that all areas of
the organization and society can have an impetus towards risk/reward
management, as opposed to purely operational job categories which
are on the decline due to development of intelligent computers.
Humans have an innate sense of risk reward and this can be leveraged to
improve organisational performance and give people a sense of
courage to push into new territory.