The real winner in the US elections: Thomas Bayes

[cpereira@ime.usp.br]

As we all know, last night was the US presidential election.  In one sense,
President Obama was the winner.  But in another sense, the real winner was
Bayesian analysis, which scored a public relations coup.

In 2008, Nate Silver developed a Bayesian model to forecast the U.S.
general election results. He won fame for correctly predicting 49 of 50
states, as well as every Senate race.  This brought him a New York Times
column and a much higher profile.

This time around, his consistent predictions that Obama was in front earned
him a considerable backlash among pundits.  While a few criticisms had
merit, most were mathematically illiterate, indignantly mocking the idea
that the race was anything other than a tossup.  Now the results are in,
and he has predicted all 50 states correctly.

People with our quantitative background can easily find flaws with this
metric. For example, a majority of states were easy to call -- nobody is
surprised by the results in Texas or California.  More seriously, his
"call" for Florida was a 50.3% probability, essentially the proverbial
"coin toss".  Serious analysis has to chalk Florida up to luck.

Nevertheless, the broader point is that Nate's high-profile Bayesian model
just experienced a very visible success. Even better, he recently authored
a book-length popular exposition of the Bayesian approach.  I purchased
that book, "The Signal and the Noise," on a recent flight.  It's excellent
reading: more technical than McGrayne's recent entry, but no less
accessible or engaging.


Charles Hogg
charles.r.hogg@gmail.com


 <cpereira@ime.usp.br>