Santa Fe Institute

The Santa Fe Institute (SFI) is an independent, nonprofit theoretical research institute located in Santa Fe (New Mexico, United States) and dedicated to the multidisciplinary study of the fundamental principles of complex adaptive systems, including physical, computational, biological, and social systems.

Small environmental changes trigger slow evolutionary processes that eventually precipitate collapse

Wisdom of the crowd corrected for bias gets accurate

In 1907, a statistician named Francis Galton recorded the entries from a weight-judging competition as people guessed the weight of an ox. Galton analyzed hundreds of estimates and found that while individual guesses varied wildly, the median of the entries was surprisingly accurate and within one percent of the ox’s real weight. When Galton published

Wisdom of the crowd corrected for bias gets accurate

Why innovation thrives in cities

Double a city’s population and its economic productivity goes up 130 percent. MIT researchers think they know why. In 2010, in the journal Nature, a pair of physicists at the Santa Fe Institute showed that when the population of a city doubles, economic productivity goes up by an average of 130 percent. Not only does total productivity

Why innovation thrives in cities

How men and women organize their (online) social networks differently

  Men and women socialize differently, and it turns out these gender differences hold true in online games that involve social interaction. A new quantitative study of data assembled from the online multiplayer game Pardus shows how females and males manage their social networks drastically differently. “It is fascinating that we maybe see traces of

How men and women organize their (online) social networks differently

More Jobs Predicted for Machines, Not People

Many workers, in short, are losing the race against the machine A faltering economy explains much of the job shortage in America, but advancing technology has sharply magnified the effect, more so than is generally understood, according to two researchers at the Massachusetts Institute of Technology. The automation of more and more work once done

More Jobs Predicted for Machines, Not People

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