FM Waves

It’s not really my argument, but I’ve just found a book where it’s stated that the outcome of the mathematical models applied to the financial markets not just let us understand better their functioning but changed the rules of it.

In, An Engine, Not a Camera: How Financial Models Shape Markets, Donald Mackenzie explains his theory about how our knowledge not just was increased, but the financial markets were re-shaped inside out. He analyses how financial theory performed in both 1987 and 1998 crises. Moreover, “financial chaos” (what’s referred to as “beyond mainstream”) is looked at too.

In definite, a theory for the implications of a theory!.

Call me a grumpy old man and I’ll tell you that but the “old” you’re right. Bloody heck!, who the hell designs car park spaces?. Do architects know anything about average car sizes?, which evil algorithm do they use to design the lay-out?… Fuck!, another mark on my car!!!.

Ok, ok, sorry architects, probably is the structural engineers that tell you where the columns have to be. All apologies!, forgive this poor grumpy young man.

Regards,

FMS

Last month I was with a colleague in a trade fair in the US when I realised we’ve lost one of our people. Then, my mate asked what should we do. The answer I gave him was the following:

1) Let’s formulate the hypothesis about where he could be.

2) With these ones let’s formulate the probability distribution of the location (being a trade fair is quite easy, since you have the map and every section is clearly indicated) and the p.d. of finding our colleague in booth “X”.

3) Generate the search path based on the higher probabilities and carry on adjusting it as more information is gathered (i.e. he isn’t on booth “Y”). This can be done by means of Bayes theorem.

Damned! He phoned the guy! Was behind us!!!

Tags: search theory

Mama said is always better to lead than to follow. However, when some people (sorry, can’t remember to cite them rightly) are already saying the internet leaders should be looking now at Web 3.0, what else remains for the mortals?.

Anyway, last January I opened a YouTube account and, hocus-pocus, included a link in this blog. See what happens next… . At the moment you’ll be able to see "nothing", until my first clip (my previous backyard in England back in 2005) is approved by YouTube. Afterwards…, we’ll see!.

The Netflix Prize is in the company’s own words the”quest” for “substantially improve(ing) the accuracy of predictions about how much someone is going to love a movie based on their movie preferences”.

I read about the prize last february on Michael Trick’s blog and the first thing I saw was the $1 Million for the winner. However, although we’re on it for the money (YES!) we don’t thing we gonna get it. So, let’s mess about it!:

_For all of you that are, like me, amateur OR-ers, I’m starting a series of posts showing where the heck I am.

……………………………………………………….

1) The data: the training set (data you have to use to create the model) is made up of more than 17 thousand text files. So, although some experts are advising on Netflix’s forums not to group them, I’ll do.

Following my own weaknesses and economist-like-mind I’m going to group the data in a single file, in order to dump it into a database (PostgreSQL, probably). Even more, as I don’t have time to learn any other language, I’ll be using VBA for Excel.

Here we go…

Sub AgrupaDatos()

Dim N As Double
Dim TextoArchivo As String

Open “C:\training_set.txt” For Output As #1

For N = 1 To 17770
Open “C:\training_set\mv_00″ & Format(N, “00000″) & “.txt” For Input As #2

Do While Not EOF(2)
Line Input #2, TextoArchivo
Print #1, TextoArchivo
Loop

Close #2

Next N

Close #1

End Sub

The module above takes about 30 minutes (Pentium 1.73 Ghz, 1GB RAM) to process the data into a file with a size of 1,92GB.

Next, the database.