In the online dating context, an algorithm can get a good idea of my taste in partners by doing a similar comparison of me to other male users. Another male user of the site will have a similar taste in women to me if we are messaging the same women.
However, while this gives the algorithm a good idea of who I like, it leaves out the important factor of who likes me — my attractiveness to the female users of the site, measured by who is sending me messages.
In our latest podcast, “The Folly of Prediction,” we poke fun at the whole notion of forecasting. The basic gist is: whether it’s Romanian witches or Wall Street quant wizards, though we love to predict things — we’re generally terrible at it. (You can download/subscribe at iTunes, get the RSS feed, or read the transcript here.)
But there is one emerging tool that’s greatly enhancing our ability to predict: algorithms. Toward the end of the podcast, Dubner talks toTim Westergren, a co-founder of Pandora Radio, about how the company’s algorithm is able to predict what kind of music people want to hear, by breaking songs down to their basic components. We’ve written a lot about algorithms, and the potential they have to vastly change our life through customization, and perhaps satisfy our demand for predictions with some robust results.
One of the first things that comes to mind when people hear the word forecasting is the weather. Over the last few decades, we’ve gotten much better at predicting the weather. But what if through algorithms, we could extend our range of accuracy, and say, predict the weather up to a year in advance? That’d be pretty cool, right? And probably worth a bit of money too.
That’s essentially what the folks at a small company called Weather Trends International are doing. The private firm based in Bethlehem, PA, uses technology first developed in the early 1990s, to project temperature, precipitation and snowfall trends up to a year ahead, all around the world, with more than 80% accuracy.
Today marked another triple-digit move for the Dow Jones Industrial Average, which closed up 272 points. Of the 45 trading days over the last two months, 28 of them (including today) have seen triple-digit moves, meaning the Dow has gone up or down by 100 points (or more) 62% of the time since July 25. The average daily move for the Dow during that time has been 188 points, or 1.6%.
Here’s a snapshot showing the performance of the Dow over the last two months:
Pretty choppy, right? I’m no stock market historian, but I’d imagine that you’d be pretty hard-pressed to find such a sustained period of volatility. Which brings up the question: what’s causing this? Obviously, there is a lot of uncertainty (and fear) in the market right now. From Europe’s sovereign debt problems, to America’s toxic political climate, to the sputtering global economy, there is a lot to be anxious about. Anxiety breeds indecision, which characterizes the bumpy market pretty well.
Using alogrithms that weight values for more than three million facts including historical events, birthdays of significant people, etc, a sophisticated computer program has determined that April 11, 1954 was the most boring day in human history.
It’s not quite the Netflix Prize — which paid $1 million to whoever could improve that company’s Cinematch recommendation algorithm by 10 percent — but there’s a new competition designed to predict magazine sales at newsstands.
Ian Ayres‘s recent book, Super Crunchers, contains an interesting description of the secret to the success of Netflix (a company that’s been discussed before on this blog). According to Ayres, Netflix’s movie recommendation algorithms are so good that they know my taste in movies better than I do. It is a source of wonderment to me just how well they . . .
That is my conclusion after seeing this Google Book Search Page for a book called How to Build Your Own Furniture. The page lists three “Related Books,” including How to Make Your Own Recreation and Hobby Rooms, How to Build Your Cabin or Modern Vacation Home, and … Freakonomics. Huh? I am trying to think of what may have fooled . . .