Collective Intelligence vs. The Wisdom of Crowds









Here, for example, is how Surowiecki describes the contexts where his ideas about the wisdom of crowds apply:


Raph Koster picks up on this aspect of Surowiecki’s model in his blog discussion:


Koster extends this key point in a subsequent blog post:

Technically, Surowiecki’s conception of “wisdom of crowds” is ONLY applicable to quantifiable, objective data. The very loosey-goosey way of using it to discuss any sort of collective discussion and opinion generation is a misrepresentation of the actual (and very interesting) phenomenon.

You can summarize the core phenomenon as “given a large enough and varied population offering up their best estimates of quantity or probability, the average of all responses will be more accurate than any given individual response.”

But this is of very narrow application — the examples are of things like guessing weight, market predictions, oddsmaking, and so on. The output of each individual must be in a form that can be averaged mathematically. What’s more, you cannot use it in cases where one person’s well-expressed opinion can sway another, as that introduces a subsequent bias into everything (which is why the wisdom of crowds doesn’t always work for identifying the best product on the market, or the best art, or the like).

Using it for subjective things, such as opinions on politics, is a mistake for sure. And using it as a shorthand to describe the continuous editing and revision that appears on Wikipedia is also a mistake.


The Wikipedia, as I discuss in Convergence Culture, depends on what Pierre Levy calls “collective intelligence.” In the classic formulation, collective intelligence refers to a situation where nobody knows everything, everyone knows something, and what any given member knows is accessible to any other member upon request on an ad hoc basis. Levy is arguing that a networked culture gives rise to new structures of power which stem from the ability of diverse groups of people to pool knowledge, collaborate through research, debate interpretations, and through such a collaborative process, refine their understanding of the world. If Koster is suggesting that the “wisdom of crowds” works badly when confronted with the challenges of politics in a democratic society, Levy sees “collective intelligence” as a vehicle for democratization, feeling that it provides a context through which diverse groups can join forces to work through problems. As I suggest throughout Convergence Culture, there are all kinds of ethical and intellectual issues to be resolved before we can say we really inhabit the knowledge culture Levy describes.

The Wisdom of Crowds model focuses on isolated inputs: the Collective Intelligence model focuses on the process of knowledge production. The gradual refinement of the Wikipedia would be an example of collective intelligence at work.

In terms of games, think about Jane McGonigal’s discussion of ARGS and the ways that a community of gamers can solve problems of enormous complexity simply by tapping expertise of individual members as needed. Here’s how McGonigal defines the Alternate Reality Game:

An Alternate Reality Game is an interactive narrative or immersive drama, played out both online and in real world spaces, taking place over several weeks or months, in which hundreds, thousands, or tens of thousands of players come together online to real play, not role-play, forming unusually collaborative social networks, and working together to solve a mystery or problem, that would be impossible to solve alone.

McGonigal’s essays and talks have identified a number of design techniques which insure that people need to collaborate in order to play the game and discuss the various mechanisms which have emerged to allow players to pool their knowledge as they work through complex challenges.

Compare this with what Edery says about tapping the wisdom of crowds through game play:

Crowd intelligence can fail (and fail spectacularly) when there’s too much information passed between members of the crowd. Members start to alter their opinions based on the opinions of others, which skews the results. The online communities that build up around any popular game would seem to promote exactly this kind of skew.

In other words, one model sees the emergence of online communities as a bug which threatens the value of the game’s research while the other sees online communities as a feature which enable us to process information in more complex ways than could be managed by any individual member. To tap the “wisdom of crowds”, Edery has to find ways around all of those things which McGonigal and other advocates of “collective intelligence” are building into their ARGs:

* Use competition to discourage group-think. The scope of information-sharing is typically more limited when players (in any game genre) are working to best other players. Of course, blocks of information-sharing players will still form (in formal teams or otherwise) but that’s not necessarily a critical problem.

* Online game communities typically form (the most persuasive) opinions about the objective aspects of a design mechanic; i.e. “you’re better off using the shotgun than the pistol, except when you’re fighting at a great distance.” But if a challenge and its feedback mechanism both incorporate real-world data, as I suggested in my earlier article, it becomes harder for any individual (or the community as a whole) to form clear strategies around.

* Encouage population diversity to decrease the likelihood of groupthink. Distributing a game in different countries and courting players of different ages are both examples.

Both “collective intelligence” and “the wisdom of crowds” offer productive models for game design but we will get nowhere if we confuse the two. They represent very different accounts for knowledge production in the digital age and they will result in very different design choices.


  1. You might also enjoy Louis von Ahn’s work, nicely presented in this video:

  2. Todd Sieling says:

    An interesting writeup, as always. I wonder if the fact that we know we are in a gaming context is key to realizing the problem-solving potential that seems to energize in play. That is, when we realize or are told that our actions and outcomes are being fed back into real-world decisions, can that awareness cripple the very benefits being sought?

  3. Interesting article. Two risks occurred to me.

    1. While playing games, I often behave in ways that would not be adaptive in “the real world”. For example, I often play very aggressively at the beginning, with the understanding that if I lose early on, I can always restart the game (or just leave). Also, I will sometimes make absurd moves simply as a way of “goofing around”.
    2. A game could really distort our decision making by emphasizing a very narrow aspect of reality at the expense of everything else. For example, with Sim City I may try to make the city as big and impressive as I can. However, this is not what I would want if I were really living in the city–I’d focus on quality of life issues, not these megalomaniacal goals.
  4. ramon reiser says:

    In my experience, with ed games or sims, and most likely w group wisdom, we ignore BF Skinner at our own peril. What we inadvertently reinforce may often come to dominate what is happening. Sim City seems to be a classic example. Almost all the boys in my high school classes preferred to bring chaos, terror, and mass destruction to building cities.

    I am not saying that adults, or teens, would necessarily do so, but we might be quite unaware of what the contingencies of reinforcement are in a given game–then again, we certainly often are unaware of what they are and how influential they are in the “real” world.