Stata Center Gets Respect

One of my very first blog posts, back in June of 2007, was about MIT’s Stata Center, a striking building designed by architect Frank Gehry. People either loved it or they hated it; that blog post was titled “the building that threw up on itself.”

Now an architecture critic, James S. Russell, has written a glowing review in Bloomberg News, saying that the critics were wrong. He went back to interview the scientists and administrators who had been working in the building all these years, and “all deemed the building a success.”  Here’s what else Russell said:

Walk with me through the ground-floor “student street,” a popular campus shortcut with ramps circling overhead, lit with shardlike skylights. You are likely to see people talking over laptops or scribbling on blackboards. Symposia often spill into the hallway as passersby stop to see why the chatter is so animated. Up a level or two, Gehry all but banished hallways. You move past lounges, open two-story seminar spaces, and eddies of whiteboard-equipped space often occupied by impromptu collaborators.

Stata’s beehive quality is intentional. At the furthest edge of research, working within the old disciplines no longer makes sense. Gehry’s team designed a building of laboratory “neighborhoods” to support communities of researchers. At Stata, linguistics, artificial-intelligence and computer scientists work together, but more boundaries need to be crossed. Stata throws people together so that every researcher has a shot at encountering the person he never thought of who turns out to have a skill that’s needed.

In my 2007 blog, I was also enthusiastic about Gehry’s building, and for the same reasons–it was designed to foster collaboration. I’m glad to read that its occupants have experienced that.

Competition Makes Groups More Creative

I just read a study by my Washington University colleague, Markus Baer, and three other authors* in the latest issue of the Academy of Management Journal. They start out by citing a history of prior studies showing that when you have different groups compete to reach a goal, the effectiveness of the groups goes up. That’s because the external threat gives a group more coherence, and a greater sense of a shared goal–two of the important properties of what I call “group flow” in my book Group Genius. But no studies have ever specifically focused on group creativity as the potential outcome of competition.

They brought together groups of four undergraduate business majors and asked them to generate ideas “to make the university more attractive to students” by focusing on two areas: the transition from high school to college, and how to improve the quality of life on campus. The researchers had a panel of three graduate students rate all of the ideas for novelty and  usefulness. Then they created three different competition situations, by varying the amount of the award for the best ideas ($4, $40, or $400) and the percentage of teams that would get a reward (top 50 percent, top 10 percent, only the top team).

The results: as they increased the intensity of the competition, the rated creativity of a team’s ideas increased. So it seems that a little bit of competition can actually enhance group creativity.

(Also see my post about the Netflix Prize)

*Baer, M., Leenders, R. T. A. J., Oldham, G. R., & Vadera, A. K. (2010). Win or lose the battle for creativity: The power and perils of intergroup competition. Academy of Management Journal, 53(4), 827-845.

Groups are Better than Individuals

If you’ve read my 2007 book Group Genius, you know about the research showing that brainstorming groups perform worse than a comparable number of solitary individuals, working alone. Groups typically generate half as many ideas as the pooled ideas of the solitary individuals.

But most of what groups are asked to do in the real world is a lot different than simply generating lists of ideas. There are many studies showing that on more complex tasks, involving knowledge of conceptual systems, groups perform better than individuals. One study* I just read today compared solitary workers to groups of 2, 3, 4, and 5, on their ability to solve a simple codebreaking task: the individual or group was told that the first ten letters of the alphabet each corresponded to one of the digits, and they had to figure out the mapping by proposing mathematical equations to the experimenter (like A + B = ?) and the experimenter gave the answer in letters. Groups of five typically solved all ten letters in 6.83 guesses–which requires them to figure out that if they use multi-digit equations in a clever way, they solve the answer faster: EED + ECD + EFG = ? This was faster than the best of five comparison individuals. Groups of four and three were also faster than the fastest of a comparison nominal group. Statistically, there was no difference in the performance of groups of 3, 4, and 5.

The performance of groups of two was statistically identical to two people working alone–suggesting that you need at least three people to get the benefits of group dynamics, but adding more above three doesn’t give you an additional benefit–at least, for this particular task.

*Laughlin, P. R., Hatch, E. C., Silver, J. S., & Boh, L. (2006). Groups perform better than the best individuals on letters-to-numbers problems: Effects of group size. Journal of Personality and Social Psychology, 90(4), 644-651.

Human Predictability

I’ve written a lot about the improvisational and creative nature of everyday life, for example in my 2001 book Creating Conversations: Improvisation in Everyday Discourse. Wouldn’t it be interesting if we could get some statistical data showing exactly how spontaneous and unpredictable the average person is during the average day?

I just learned about this study*, published in February 2010 in Science, that uses cell phone usage data to measure how predictable our daily movements are. The cell phone companies have to know which cell phone tower you’re connected to when you make each call, so the researchers used data for about 45,000 people (the data were “anonomyzed” meaning no loss of privacy). They used rather complex statistical algorithms to compare the actual movements with the expected trajectories of movement if they were completely random.

For each user, they calculated that user’s predictability of movement–the percentage of movements that could be predicted. The average number across all users was a whopping .93, meaning that only 7 percent of the daily movements were not predictable! They then compared users who tended to stay in a 10 kilometer radius, with those who drove around a lot in a given day. Even the people who traveled a lot had a predictability of .93.

But we don’t always go from one location to the same next location! It turns out the predictability is largely based on the temporal information–the daily times that we move. Relying only on spatial data results in no predictability at all.

More specifically: In a given hour of the day, the data predict which cell phone tower you will be next to 70 percent of the time. That number peaks at .9 in the evening, when most people are at home, and is most variable during rush hour and lunchtime, when people are moving around more.

The researchers conclude by saying that 93% is “an exceptionally high value rooted in the inherent regularity of human behavior” (p. 1021) and that we are predictable “despite our deep-rooted desire for change and spontaneity”. And this predictability did not vary by how far you typically travel, nor did it vary by any demographic characteristics (age, gender, first language, population density of address, or urban vs. rural).

*Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327, 1018-1021.