David Friedman is a friend who likes to talk about how social networks and social interactions can support innovation. The challenges are dual. One, how do we get the right talent together? Two, how do we get these talented individuals interact in the right ways?
Crowd-sourcing lets individuals generate ideas, but better innovation can come from the interaction of people with a diverse set of skills and interests. How can such a group of strangers, be assembled, and how can it function well together and be productive in a minimal amount of time?
The quality of results in collaboration problem-solving is a function of two variables: the diversity of input and the quality of the interactions among the people. Suppose we could bring together many diverse people and use outstanding group problem-solving methods. Presumably, they would get better results than a relatively small group that works well together -- such as a good team -- or a diverse set of individuals who do not work together.
Here are some examples of collaborations that bring together larger groups than teams and also use methods that allow individuals to work together
(1) Innocentive. This well-known platform for solving problems works when individuals who think they have the answer to a problem can submit and be paid if their answer is selected. Most of these problems are solved by an individual who sees fairly quickly that he or she has some knowledge that can be applied to the problem. Not much teamwork is at work.
A friend of mine, David Friedman, talks about Innocentive Chinese style, which is a reference to groups of participants in China collaborating to decide upon which problems to attack. The variety of problems they attack comes from the stream of Innocentive problems.
In Good to Great, Collins and Porras write about getting the right people on the bus and then determining the appropriate strategy. By taking advantage of the stream of Innocentive problems, the Chinese groups have gathered the right talent and can also look for the problems that this particular assemblage is best positioned to solve. The laws of probability suggest that his method will yield greater fruits than what individual Innocentive participants can do.
The popular Netflix Prize challenge produced ever larger groups of collaborators as it neared its end. As the teams grew, they were able to use traditional, novel, and collaborative problem-solving techniques from larger groups that in some ways were unique to the problem. They mathematically combined algorithms (their solutions to the problem) to get better results.
Open Space methods bring people with a stake in the problem together and then let them work together on what they believe are the important elements. They yield results that cannot be easily predicted but are very powerful.
Polymath was a high-level mathematical collaboration that yielded outstanding results through open collaboration of many individuals. Conducted on a blog and guided by a set of rules that encouraged participants to share ideas that were not complete but upon which others could build (or refute), Polymath promised to yield optimal ways to structure their interactions.
Crowd-sourcing lets individuals generate ideas, but better innovation can come from the interaction of people with a diverse set of skills and interests. How can such a group of strangers, be assembled, and how can it function well together and be productive in a minimal amount of time?
The quality of results in collaboration problem-solving is a function of two variables: the diversity of input and the quality of the interactions among the people. Suppose we could bring together many diverse people and use outstanding group problem-solving methods. Presumably, they would get better results than a relatively small group that works well together -- such as a good team -- or a diverse set of individuals who do not work together.
Here are some examples of collaborations that bring together larger groups than teams and also use methods that allow individuals to work together
(1) Innocentive. This well-known platform for solving problems works when individuals who think they have the answer to a problem can submit and be paid if their answer is selected. Most of these problems are solved by an individual who sees fairly quickly that he or she has some knowledge that can be applied to the problem. Not much teamwork is at work.
A friend of mine, David Friedman, talks about Innocentive Chinese style, which is a reference to groups of participants in China collaborating to decide upon which problems to attack. The variety of problems they attack comes from the stream of Innocentive problems.
In Good to Great, Collins and Porras write about getting the right people on the bus and then determining the appropriate strategy. By taking advantage of the stream of Innocentive problems, the Chinese groups have gathered the right talent and can also look for the problems that this particular assemblage is best positioned to solve. The laws of probability suggest that his method will yield greater fruits than what individual Innocentive participants can do.
The popular Netflix Prize challenge produced ever larger groups of collaborators as it neared its end. As the teams grew, they were able to use traditional, novel, and collaborative problem-solving techniques from larger groups that in some ways were unique to the problem. They mathematically combined algorithms (their solutions to the problem) to get better results.
Open Space methods bring people with a stake in the problem together and then let them work together on what they believe are the important elements. They yield results that cannot be easily predicted but are very powerful.
Polymath was a high-level mathematical collaboration that yielded outstanding results through open collaboration of many individuals. Conducted on a blog and guided by a set of rules that encouraged participants to share ideas that were not complete but upon which others could build (or refute), Polymath promised to yield optimal ways to structure their interactions.
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