I thought the market for complexity books had been saturated, but here’s another one: A Crude Look at the Whole by John H. Miller. (WSJ review here.)
The first wave of complexity books was in the mid 1990s:
The heyday of complexity books was just after 2000 (my own book appeared in 2005):
In just the past few years, we have
According to Ronald Bailey’s WSJ review, Miller’s book covers familiar ground. Like my 2005 book, he argues that “societies are complex systems”; that social phenomena “emerge unpredictably from components”; that “simple parts interact in complex ways to create an emerging whole”. His examples of emergence from complexity are familiar from these earlier books: biological evolution, markets, the Internet, political protests. Bailey’s review is politely critical of the book; he says “it’s hard to see how complexity science is much help to current policy makers or citizens.” I disagree; I think that understanding complexity and emergence has incredible value, especially in understanding social systems. Maybe Miller’s book isn’t the first one you should read, but the long list of earlier books (and their strong sales) demonstrates that this research is helping lots of people.
Professor Marvin Minsky of MIT died at 88 on Sunday January 24. He was one of most important scientists in artificial intelligence (AI): a field of research studying how to develop computers that simulate human thought and behavior. Minsky founded the world’s first AI Lab at MIT in 1959, along with John McCarthy.
Minsky was my undergraduate thesis advisor at MIT, and what I remember most about him was his fascination with music. I graduated in 1982 with a degree in computer science, and I did my thesis research on Minsky’s “Music Cognition” project. His grad student David Levitt was my manager, and we worked on the first-ever screen-based music composition editor. You can display sheet music on any computer these days, but in 1981 home computers could only display white letters on a black background. The AI Lab had radically different computers, with bitmap screens that could display pictures; text in different typefaces and point sizes; pull-down menus; everything controlled by a mouse (with three buttons). These advanced graphics allowed us to display music notation, and allowed composers to edit music on screen.
(BTW it wasn’t the Xerox PARC smalltalk computer, that Steve Jobs famously “borrowed” to create the Apple Mac; our computers were invented and built at MIT and they were called LISP machines.)
Minsky often worked together with Seymour Papert, the MIT professor who developed the LOGO programming language for children. They both worked in the same building in Tech Square (in Cambridge’s Kendall Square); the AI Lab was on the 9th floor and the LOGO lab was on the third floor. One day on my way down in the elevator, I decided to stop at the third floor to check it out; that was the one time I met Papert and saw the original plexiglass LOGO robot.
Back in the early days, computer scientists were idealistic; they believed in open access to everything and everyone. For example, no one’s personal files were password protected. Because I used the same computer as Dr. Minsky, I could access his folders and read every document he was working on. In 1981, I went into his folders and discovered a draft of his book Society of Mind (it wasn’t published until 1985). The book argued that intelligence emerges from the interaction of many smaller, non-intelligent entities inside the mind–something like a “society”. This concept made a huge impact on me, and I kept thinking about emergence and complex systems for two more decades. In 2001, I published my first journal article on the topic, “Emergence in sociology,” and in 2005, I published a book exploring these ideas, Social Emergence: Societies as Complex Systems.
That’s the kind of influence a great scholar has on the world–it lasts for decades.
You’ve got to read the excerpt from Matt Ridley’s new book in today’s Wall Street Journal. Just released this week, his book is called The Evolution of Everything: How New Ideas Emerge. I have a lot of respect for his previous books, so I’m delighted to learn that his new book makes the same points as my 2007 book Group Genius.
Here are the key features of innovation, described in both of our books:
- The stories we hear about genius inventors, like Thomas Edison inventing the light bulb, are always myths. Ridley and I both describe the real history of the light bulb, which involves lots of people way before Edison. (Group Genius, pages 110, 196)
- “Innovation emerges from the bottom up,” I write in Group Genius (page 16). I show that innovation emerges from self-organizing systems, and this is Ridley’s main point, too.
- Ridley writes that innovation is “incremental” rather than “revolutionary.” That’s why I called one of my chapters “Small Sparks”: to emphasize that innovation doesn’t come from a big flash of insight. “Successful creators know how to keep their sparks coming in a process that unfolds over time” (Group Genius, page 97).
- Ridley describes the historical research on multiple discovery, as I do on pages 192-193, with this example: “In the 1920s, numerous teams invented television in parallel.”
- Ridley argues that patent protection is too broad and is based on the mythical view of the lone inventor. I make the same point on pages 176-224, especially pages 221-225: “Current policy favors linear, centralized innovation and blocks the natural rhythm of innovation”.
- Ridley demolishes the idea that innovation comes from a linear process; this is the most important point of Group Genius (for example, pages 158-159, “Beyond Linear Innovation”)
Ridley’s WSJ excerpt is filled with great stories of real innovations. I come to the same conclusions, with some of the same historical examples, and also by drawing on the science of creativity. Inspired by my studies of jazz and improv theater, I think of creativity as improvisation. Group Genius argues that the most creative improvisations are non-linear, emergent, unpredictable, and inefficient. Ridley has a bit more to say about the political and economic implications of this new, more realistic, understanding of innovation (for example, he concludes that government doesn’t need to fund scientific research). I have a bit more to say about how you can use this research to become more successfully creative, both on your own and in teams. It’s cool that Ridley and I come to the same conclusions from really different directions. If you like Group Genius, you really should check out The Evolution of Everything. (I’ll post a review after I’ve read the whole book.)
I’ve just read a wonderful research article called “Team implicit coordination processes”.* Most studies of how team coordinate have focused on planning and communication; these are both explicit coordination, meanint that everyone is consciously aware of what they’re doing, they’re trying to do it, and they’re talking about it. The authors of this article claim that explicit coordination only explains relatively static teams, when the situation isn’t changing very rapidly. Implicit coordination happens “when team members anticipate the actions and needs of their colleagues…and dynamically adjust their own behavior accordingly, without having to communicate directly with each other or plan the activity” (p. 164).
That’s exactly what goes on in a jazz ensemble or an improv theater group, the super-creative groups that I’ve spent years studying (see my book GROUP GENIUS). Teams have to implicitly coordinate to handle rapidly changing environments when their tasks are highly interdependent; teams that are implicitly coordinating talk a lot less about what they’re doing and what they should do next. (This reminded me of a conversation I had at Harvard recently with Professor Rob Huckman, who has studied surgical teams. Surgeons say that in the best teams, no one is talking…that’s implicit coordination!)
Teams that have this down do four things: (1) each member provides task-relevant information even before they are asked for it; (2) team members share the workload without being asked; (3) everyone is monitoring the progress of the activity and the performance of their teammates; and (4) each person adapts behavior to what they expect the others will do.
The authors argue that implicit coordination can only work if the group creates an “emergent team-level knowledge structure” that they call a team situation model. The model includes shared knowledge like the team’s goal and the roles of each participant. Because of my own studies of social emergence, I agree when the authors claim that the situation model is “an emergent group property characterizing the team as a whole” (see my 2005 book SOCIAL EMERGENCE for more details).
*Ramon Rico, Miriam Sanchez-Manzanares, Francisco Gil, and Cristina Gibson. 2008. “Team implicit coordination processes: A team knowledge-based approach.” Academy of Management Review, Vol. 33, No. 1, pp. 163-184.