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## The Fourth Quarter

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• One of my favourite authors at the moment is Nassim Nicholas Taleb. So he mostly speaks about finance, because that s what he knows, but underneath he is
Message 1 of 1 , Sep 18 4:38 PM
One of my favourite authors at the moment is Nassim Nicholas Taleb.

So he mostly speaks about finance, because that's what he knows, but
underneath he is mostly talking about statistics and risk. And that is
what we all deal with.

His previous book "The Black Swan" in some senses wasn't very
useful... it can be abbreviated as "We are really Bad at prediction,
much worse than you believe."

His latest essay is actually quite handy. It provides a map of where
we are going to be startlingly Bad at predicting.

http://www.edge.org/3rd_culture/taleb08/taleb08_index.html

Many Agilisto's will say, "Yip, he is right, thats why our practices
work".

Others may look at Taleb's essay and get an "Aha!" moment and finally
realize why some of the Agile practices work.

He suggests you divide problems on the basis of moments of a random variable.

If your decision is a "yes/no" choice, it is simple. Will the project
be finished by the 5th of December 2008?

If your question is based on the value of a random variable, it is more
complex. What will be the completion date of the project?

If your question is based on a higher moment of a random variable, it is very
complex. What will be the ROI of a project?

Then look at the nature of the randomness... Is it fat tailed, or well behaved?

For non-statistical types a probability distribution can be fat or
thin tailed. The one you learned about in the stats course you have
mostly forgotten was a thin tailed one. (Gauss / Normal distribution).

Odds on if you did any stats course they went on for hours about thin
tailed distributions, because they can do the mathematics for them.

Unfortunately most real world distributions are fat tailed.

If you have a 1000 guys in the company, the average weight of
employees is simply not going to shift by much if you employ the
fattest guy in the world. (Fat guys come from a thin tail probability
distribution.)

If you look at a 1000 random project case studies, the average project
overrun is going to massively shift if you add the worlds largest
project overrun.

ie. Things like food requirements for project workers are random
variables from what Taleb calls "mediocristan".

Things like time to completion are from "extremistan".

So if divide your problems in to quadrants like this....

Simple Payoffs | Complex (Higher Moment) payoffs

Thin tail distribution Predictable | Less predictable
Fat tail distribution Less predictable | You're utterly stuffed.

Exercise for the Reader...

1) Catalogue the random variables in your work situation and
categorise them as from mediocristan or extremistan.

eg. Time to complete an item of work - Extremistan

Programmer Productivity - Very high variance, but probably
Mediocristan.

Security Risks - Extremistan. (No valid distribution on attack models, motivations etc.)

Exchange Rate fluctuations - Extremistan

Programmer Defect rates - Not sure. Maybe mediocristan for simple monothreaded programs.
Extremistan for concurrent.

Probably "Programmer attribute XXX" for what ever XXX is
truncated. Truncated in one sense because we don't hire / or we
fire / or we "transfer" the worst.

Truncated in another sense because our biological meatware is
from mediocristan.

....

2) Catalogue (crudely) the predictions you are required to make by moment of the random variables involved.

M0
Will the project be completed by christmas?
Do we need an extra hard drive?
Is the CPU fast enough?

M1
How long will the project take?
How much disk space will we use?

Number of page hits per day is correlated to number of registered
users. Look at our records and tell me what is the ratio?

M2
Number of page hits per day is correlated to number of registered
users. What is the error bars on that number?

We have N registered users, what will be the worst case response time?

3) Which predictions have you made in your last project proposal that
were in the "utterly stuffed" quadrant?

Note the gotcha, we are appalling bad at estimating what the worst
case response time will be, but sort of OK at estimating "Is the CPU
fast enough". How does that work?

Easy. If the CPU was very very slightly too slow, it wasn't fast
enough. If the response time was longer than the age of the
universe... the CPU still wasn't fast enough. ie. Whoever asked the
question, probably was asking the wrong question.

John Carter Phone : (64)(3) 358 6639
Tait Electronics Fax : (64)(3) 359 4632
PO Box 1645 Christchurch Email : john.carter@...
New Zealand
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