I'm still at the O'Reilly Emerging Technologies conference in San Diego. James Surowiecki's talk today, Independent Individuals and Wise Crowds, or Is It Possible to Be Too Connected? Was the highlight of the day, in my opinion. I took near-verbatim notes, published below. Note: by near-verbatim, I mean that I captured almost every thought Surowiecki expressed, using his language for the most part, with a few interpolations and omissions that don't affect the meaning.

Surowiecki:

This talk won’t end in a coherent answer. But I wanted to raise specific problems or issues around questions of collective intelligence, collaboration, collective action, a whole host of themes that you’ve heard about here.

One of the major themes of the last decade: a lot of interest in various ideas about collective action and collaboration. Slashdot, Google. Prediction markets, flash mobs, Wikis, Linux, del.icio.us, are all examples of projects that bring together large groups of people to work together explicitly or implicitly. Google, for example, harvests group intelligence about the Web. All these things have something in common in the way we experience them. In the same sense, there’s been a lot of writing about network effects, emergent behavior (Steven Johnson), smart mobs (Howard Rheingold), the wisdom of crowds (the title of my book) and ewallet in this article. There’s an affinity between all these ideas.

But I want to talk about the differences between these things – it’s useful to think about how these things are not alike. The ontology may be overrated, but classification still has used, in particular, because different problems require different solutions. Josh Schachter talked about this. If you use the wrong kind of collective action, you can end up with worse problems than the ones you set out to solve. There’s been a lot of fuzzy thinking about what we mean when we talk about collective intelligence, network, and interaction. I want to parse these distinctions.

In The Wisdom of Crowds, I wrote about the power of groups under certain circumstances to be remarkably intelligent. A model of collective intelligence: a large group of people reflecting diverse opinions offering judgments independently with some mechanism to aggregate the judgments, collectively ending up with an intelligent outcome. The crowd contained a lot were experts, but many were merchants, family members. Galton collected guesses and took the average. The group had guessed that the ox would way 1,197 pounds. And in fact, it was 1,198. The group’s judgment was virtually perfect. The argument in the book is that this is not a coincidence, nor is it confined to livestock. It can be seen at work in a far more complex phenomenon.

At the racetrack, the odds on horses predict almost perfectly how likely a horse is to win. Horses with 3:1 odds win about a quarter of the time. The favorite wins most often, the second favorite wins second most often, etc. Odds are determined collectively through everyone’s bets. Probabilistic judgments, it turns out, are excellent. Corporations have experimented with this model. Eli Lilly has an internal stock market to predict which drug candidates are most likely to make it through Phase III clinical trials. Their whole business is built on this question. It’s open to 100 “semi-experts,” and collectively they can recognize which candidates are viable and which are not, well in advance.

Contrast this to Linux. It’s a large group working on problems, but ultimately one individual writes the piece of code that gets incorporated. The decision-making process is centralized. In the end, just a few people or even only one person decides what goes into the kernel. This is a different kind of collaboration from collective judgment.

Contrast this to the anthill, as the metaphor for human behavior (Stephen Johnson uses this example in Emergence). Ants don’t know anything. Remember the scene from Antz (or maybe it was A Bug’s Life): the leaf falls in the middle of the long line of ants and the ant panics, doesn’t know what to do. Though no individual ant knows much, their interactions produce quite stunningly exceptional results. Ants are remarkably good at finding food with the least amount of energy. The way they do this is by following straightforward rules, similar to the way birds flock, and they pay enormous attention to those around them. The interaction is the essence of the intelligence.

Human beings are not ants. We don’t have the biological programming or tools that ants have. The way ants find food has to do with their formic acid secretions; the more trails, the more signals; the entire colony can find its way to the food source. We have no equivalent to this. For us, interaction is incredibly problematic, especially when it comes to group behavior. If there is too much interaction among human beings, groups end up being less intelligent than they would otherwise be. The book has quite a bit about small groups. Put a bunch of smart people into a room, and they emerge dumber than when they went in.

Why can interaction have such negative consequences? Firstly, human beings herd. They tend to stick with what others are saying. “It is better to fail conventionally than to succeed unconventionally.” – Keynes. Humans like the comfort of the crowd. Mutual fund managers herd, even though their whole business is predicated on doing better than those around them. It’s a way to appear reasonable. If you want to look that you have a pretty good idea of what you’re doing, do what those are around you are doing.

Second, humans imitate. We are imitation machines. The example I use in the book: social scientists put a few guys on a street corner and had them look up at the sky. Pretty soon about a third of the people passing stopped and started looking up at the sky. When the scientists had five or six guys looking at the sky, 60 percent of the passers-by looked. When it was 10 or 12 people, 85 percent looked. People do this because we assume that if a lot of people are doing something or think it’s valuable, it very likely is valuable. That’s a tremendously useful assumption. The problem is that when human beings imitate slavishly or without considering what they’re doing, you get a bunch of people looking on the street corner looking up at an empty sky.

Scientists call this an information cascade. You’ve read The Tipping Point. It’s the notion that once an information cascade gets going, it becomes very hard for people making a decision later in the process not to do what everyone else has done. Say you have two restaurants, both empty, and there’s no reason to think that one is better than the other. You go to the street corner, look in, and decide I’ll go to this one. The next couple comes along and has the same problem.

They see you’re in one restaurant, and they say we’ll go there. Pretty soon everyone assumes there is some value to the fact that everybody is in one restaurant, even there wasn’t. It can be proved mathematically that after a certain point it becomes rational to do what everyone else is doing, also if you have information that suggests the opposite is true. As long as you assume that everyone else is rational, that is. That’s what The Tipping Point is all about. People no longer making decisions on their own, but only because those in front of them have done the same. Quality has little to do with what ends up getting chosen. Collective decisions may not be in any sense tied to class. The result: the group as a whole becomes less intelligent. On the web, the critical factor in a site getting more links is how many links it already has. In that model, there is no guarantee that the group as a whole is intelligent. The wisdom of crowds does not emerge.

Pascal said all problems in the world arise from one simple fact: a man cannot stay in his room and think quietly by himself. That’s not what I think we should do. Interacting has enormous value, for a variety of reasons. You may have information that would be valuable to me. Our exchange may lead to a more diverse and intelligent group forecast. Sometimes feedback is useful: Your judgment may sound crazy, but perhaps I’ve overestimated the odds on my horse? Finally, some problems need to worked on collectively—for example, in team sports.

In computer science experiments at the University of Michigan, a researcher, Scott Page, had his agents compete until they differentiated into three groups, Dumb, Intelligent, and Random. Then he had them solve the problem as groups. The Intelligent group outperforms the Dumb group, but not by very much. But the Random group almost always beats the Intelligent group. Page’s theory is that the reason for this is that even if the less intelligent groups know less, what they know is different.

This has significant implications for the way decision making works inside organizations. Make groups that range across hierarchies. The conclusion is that you actually can be too connected, if the connections are wrong and if they’re reinforcing your existing prejudices rather than altering them. You can pay to much attention to those around you, even if they’re brilliant. The flip side of Pascal’s isolation is the cacophony you find on the net; it bombards you with many voices. Separation and dissonance, interestingly, allow you to arrive at the same place: independence.