FM Waves

…I already live in an Idiocracy. I don’t watch too much TV, regretfully not even my beloved ‘IT Crowd‘, ‘Discovery Channel’, ‘History Channel’, CNN (Spanish version), other news, and some stand-ups (’monólogos’ in Spanish), so I had to find out about "Idiocracy" (the movie) thanks to ‘Soy Geek‘. It seems I don’t have to regret that much have had no such notice of the movie, since the box office was such low they took it out inmediately from the screens (that is, not much advertising on this masterpiece); this is what I call a ‘death by success’: yes, it was a success, they anticipated the future in such a way we should be categorise the movie as a ‘reality show’ rather than ’sci-fi’ (that’s why everyone hated the movie!!!).

From OR perspective what really liked was to see the Gaussian Curve to show the military listening to the doctor that was preparing the experiment the guy he’d choosen to be frozen was ‘AVERAGE‘. He damned really was (that point where average equals median equals mode)… until he awakes… the distribution has changed… and he’s now in the upper tail!!!, ‘King of the World’!!!.

Morale: No matter how you’re, you’ll always be who you’re in comparison to others.

Hey!, No one told us Statistics was a good subject for ‘morales’!. I personally don’t like this morale.

Some of us have been commenting on our posts about Numb3rs getting excited because of the nature of the series, a mathematician that uses their knowledge to solve criminal cases. Well, we’ll be advocating now for another TV series OR-er that has been neglected over the last years: Dr House (yes, he’s a Dr rather than Charlie’s PhD).

Just let me know your comments, since I’m considering House as a good example of soft O.R. application.

While I’m trying to juggle around with the data set offered by Netflix for the quest of improving their Cinematch algorithm I’m in my own quest for getting the theory behind the real model, the structure that resides behind those 2GB of user and movie ids, dates and so on.

Years ago I co-authored a paper about tastes and preferences, so I liked to carry on with this research, in order to give light to the matter (i.e. 40 movie features can be resumed in just one, “the rating”; people’s ratings are inconsistent; blah blah blah); by the way, it’s because, at the end of the day, ratings are just a set of preferences (ordinal, transitive, reflexive, but are they complete?). This doesn’t mean I’ll stop researching through machine learning, but that I’m opening two fronts.

For those fighting along my side, I’d recommend the following readings:

_Varian, H. (1992). Microeconomic Analysis. W. W. Norton & Company; 3rd edition.

                 Chapters: 7 (Utility Maximization), 8 (Choice), 19 (Time).

_Rabin, M. (1998). “Psychology and Economics”. Journal of Economic Literature, Vol.XXXVI, pp.11-46.

_Rieskamp et al. (2006). “Extending the Bounds of Rationality: Evidence and Theories of Preferential Choice”. Journal of Economic Literature, Vol.XLIV, pp.631-661.

It doesn’t mean these articles are going to help solve the problem, however are going to help understand why when we do this this and that, the result is such a given RSME.

If you’re an A+ dummy (aka almost-not-a-dummy) you can try with this software (Varozhka), created by Eugene Rymski, to play around with the Netflix Prize dataset.

Brilliant!

Mr Smith, XVIII century’s great grand-father of modern economics, devised the power of the market as the main driver (anyone for the “invisible hand”?). As in “A Beautiful Mind” we can hear: “every man for himself”, because the own interest will tend to get the main group’s interest. Following Mr Nash’s intervention, in fact, prior group negotiation is needed in order to obtain individual interest (this is the basis for collaborative games which it was thought to be the foundations for cutting edge game theory, however, later on, John Nash demonstrated that it really was non-collaborative ones). Let’s watch it: