Pattern #16


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Crowdsourcing Card - version 1


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Within any given problem or possibility there are many points of information, innovation, influence, and resources. So address the whole by inviting all these sources to come forth with their own contributions to the enterprise, unconstrained by official roles and assumptions and then weave them into greater wisdom and effectiveness.

Related: 2 Appreciative Thinking, 11 Communal Intelligence, 19 Distributed Intelligence, 27 Full Spectrum Information, 45 Powerful Questions, 66 Well-Utilized Life Energy, 67 Whole System in the Conversation

Going deeper …

This is an edited version of the video on this page.

This is a “many points of light” kind of thing. Instead of seeing a problem as an abstract thing or just people fighting, we see it as a living ecosystem, or network of nodes and those nodes are alive and we can get information and energy from them.  This is not just relating to problems, but also to possibilities. If a possibility were to become real or in case a negative possibility were to be prevented, these people or these potentials points of influence in the system could be accessed and brought into their more positive role in addressing the situation.

Sometimes it’s very simple, like, “OK, there’s lots of people who have information on something, and we can use them to get that information.“ This is like people described in the book “The Wisdom of Crowds” using crowdsourcing in “prediction markets” to predict what’s going to happen or what the next breakthrough is going to be. All sorts of people get in and put in their two cents about what the next big breakthrough is going to be in technology, for example. It turns out, when lots of people do that – and they’re not talking together, they are just independent agents – their predictions end up being startlingly accurate. That’s what the book “The Wisdom of Crowds“ talks about.

And then there’s crowdfunding. We say, “Okay, we want to do a project and find many people who might want to put in a few dollars to support that.” And if we do it really well, we end up with thousands or millions of dollars. So again the sense that there’s all these little pieces that can be pulled together into something much larger.

We address the whole by inviting everyone involved to come forth with their own contributions to the enterprise, unconstrained by official roles. It’s not like, “Okay, you are going to contribute this because you’re a person in such and such a role” – you’re a businessman, or you’re a policeman, or whatever. It’s like the more we can just loosen up and draw people in in whatever way makes sense to them, the better we’ll do.  They may have a lot of gifts to give that derive from their role but, they’re not constrained by their role. They are full human beings.

I’ve sometimes given a talk to a crowd of a hundred people and I can sense the immense potential in that room that most of people in the room are unaware of. I say, “How many people here are well connected to at least 100 people?“ Half the room’s hands will go up. Sometimes practically everybody’s hands will go up. Then I say “Now multiply that out. Look at all those hands and realize how many people – if we chose to – we could reach just from us in this room.“ It’s amazing nowadays the kinds of connectivity that are represented even by a small number of people.

That’s an awareness that comes from the potential for crowdsourcing. And it’s not that you go to somebody because they are the official source of this kind of information.  You just do it from the whole crowd with no preliminary assumptions of who might know what.  You just open it up and see who comes to the party.

Then once people come, there’s the challenge to weave that into greater wisdom and effectiveness which sometimes can be done relatively automatically. Look at Amazon:  You go to Amazon and you want to buy an umbrella, so you are looking at a particular umbrella, and it says, “People who bought this umbrella also bought this” or “People who looked at this umbrella ended up buying these things.”

That information was put together by an algorithm, not by any person pulling it all together.  That phenomenon is called “stigmergy”. It is the same phenomenon that governs ant colonies. Ants leave traces of chemicals that mean things to other ants. An ant comes up to that trace and it knows it is supposed to go back to the ant colony, or to go off in this particular direction where the previous ant went, or it is supposed to pick up this ant and take it to to the ant graveyard. And what’s happening on Amazon is that we are leaving a trace that we looked at this, that we bought that. An algorithm is weaving that into something that then feeds back to us and/or the people that follow us: “Here’s the traces of other people. Why don’t you just act like they did?!”

So all that – and the rating systems we use online – are all forms of crowdsourcing used for individuals. And of course the greatest example of crowdsourcing collectively is Wikipedia. Information from a lot of people who are and aren’t experts gets woven together by their engagements with each other – not at the same time, but over time – into this massive encyclopedia. There’s a bunch of people who are constantly working it over, to make it more accurate like a traditional encyclopedia. But it’s massive, there are millions of people putting stuff on there, taking stuff off and reframing it and all that. So that’s the spirit of crowdsourcing. We now have technology to reach out widely to many people who otherwise are not connected and to access them for information, influence, and resources.

Video Introduction (9 min)

Examples and Resources  I participated once in a really interesting effort to crowdsource the focus of an upcoming conference.  Codigital does this by using paired comparisons between two people’s responses to a given question, such as “What should we talk about at our conference?”  It presents the two answers to you and asks “Which of these is most important?“ Participants are generating new responses for a collective list regarding whatever the topic happens to be, and then comparing them according to the instructions from the algorithm. The algorithm keeps dishing up new paired matches of things people put on the list. Out of all that the algorithm is generating a prioritized list of which things are more important than other things.

Voting is one of the epistemic processes studied by Yale professor Helen Landemore. [Epistemic means “relating to valid knowledge”.]  She highlights the epistemic properties of making a collective decision by integrating many independent inputs. I don’t know about the qualities of that that make it epistemically valid, but she covers that in her book Democratic Reason. I’ve always been down on voting. This might change once I get more acquainted with her work. Voting is, after all, definitely another example of crowdsourcing.


  • James Surowieki, Wisdom of the Crowds
  • Helen Landemore, Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many