The Advantages of Closing a Few Doors

From the New York Times, a piece about Predictably Irrational from Dan Ariely. I’m somewhat fascinated by the idea of our general preoccupation with holding on to things, particularly as it relates to retaining data (see previous posts referencing Facebook, Google, etc.)

Our natural tendency is to keep everything, in spite of the consequences. Storage capacity in the digital realm is only getting larger and cheaper (as its size in the physical realm continues to get smaller), which only seeks to feed off this tendency further. Perhaps this is also why more individuals don’t question Google claiming a right to keep messages from their Gmail account after the messages, or even the account, have been deleted.

Ariely’s book describes a set of experiments performed at M.I.T.:

[Students] played a computer game that paid real cash to look for money behind three doors on the screen… After they opened a door by clicking on it, each subsequent click earned a little money, with the sum varying each time.

As each player went through the 100 allotted clicks, he could switch rooms to search for higher payoffs, but each switch used up a click to open the new door. The best strategy was to quickly check out the three rooms and settle in the one with the highest rewards.

Even after students got the hang of the game by practicing it, they were flummoxed when a new visual feature was introduced. If they stayed out of any room, its door would start shrinking and eventually disappear.

They should have ignored those disappearing doors, but the students couldn’t. They wasted so many clicks rushing back to reopen doors that their earnings dropped 15 percent. Even when the penalties for switching grew stiffer — besides losing a click, the players had to pay a cash fee — the students kept losing money by frantically keeping all their doors open.

(Emphasis mine.) I originally came across the article via Mark Hurst, who adds:

I’ve said for a long time that the solution to information overload is to let the bits go: always look for ways to delete, defer, or otherwise avoid bits, so that the few that remain are more relevant and easier to handle. This is the core philosophy of Bit Literacy.

Put another way, do we need to take more personal responsibility for subjecting ourselves to the “information overload” that people so happily buzzword about? Is complaining about the overload really an issue of not doing enough spring cleaning at home?

Sunday, April 27, 2008 | retention  

Visualizing Data Book CoverVisualizing Data is my 2007 book about computational information design. It covers the path from raw data to how we understand it, detailing how to begin with a set of numbers and produce images or software that lets you view and interact with information. When first published, it was the only book(s) for people who wanted to learn how to actually build a data visualization in code.

The text was published by O’Reilly in December 2007 and can be found at Amazon and elsewhere. Amazon also has an edition for the Kindle, for people who aren’t into the dead tree thing. (Proceeds from Amazon links found on this page are used to pay my web hosting bill.)

Examples for the book can be found here.

The book covers ideas found in my Ph.D. dissertation, which is the basis for Chapter 1. The next chapter is an extremely brief introduction to Processing, which is used for the examples. Next is (chapter 3) is a simple mapping project to place data points on a map of the United States. Of course, the idea is not that lots of people want to visualize data for each of 50 states. Instead, it’s a jumping off point for learning how to lay out data spatially.

The chapters that follow cover six more projects, such as salary vs. performance (Chapter 5), zipdecode (Chapter 6), followed by more advanced topics dealing with trees, treemaps, hierarchies, and recursion (Chapter 7), plus graphs and networks (Chapter 8).

This site is used for follow-up code and writing about related topics.