In their research paper, Harnessing Crowds: Mapping the Genome of Collective Intelligence, Thomas Malone and his two co-authors, Robert Laubacher, a research scientist at M.I.T., and Chrysanthos Dellarocas, a professor at the University of Maryland, use a biological analogy in calling the design patterns of collective intelligence systems “genes.”
They studied the genelike building blocks in more than 250 examples of collective intelligence enabled by the Web. The intent, they write, is to provide a systematic framework for thinking about collective intelligence, so “managers can do more than just look at examples and hope for inspiration.”
I love this multi-disciplinary approach to thinking.
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Google. Wikipedia. Threadless. All are well-known examples of large, loosely organized groups of people working together electronically in surprisingly effective ways. These new modes of organizing work have been described with a variety of terms—radical decentralization, crowd-sourcing, wisdom of crowds, peer production, and wikinomics. The phrase we find most useful is collective intelligence, defined very broadly as groups of individuals doing things collectively that seem intelligent.
By this definition, collective intelligence has existed for a very long time. Families, companies, countries, and armies are all groups of individuals doing things collectively that, at least sometimes, seem intelligent.
But over the past decade, the rise of the Internet has enabled the emergence of surprising new forms of collective intelligence. Google, for instance, takes the judgments made by millions of people as they create links to Web pages and harnesses that collective knowledge of the entire Web to produce amazingly intelligent answers to the questions we type into the Google search bar.
In Wikipedia, thousands of contributors from across the world have collectively created the world’s largest encyclopedia, with articles of remarkably high quality. Wikipedia has been developed with almost no centralized control. Anyone who wants to can change almost anything, and decisions about what changes to keep are made by a loose consensus of those who care. What’s more, the people who do all this work don’t even get paid; they’re volunteers.
In Threadless, anyone who wants to can design a T-shirt, submit that design to a weekly contest, and vote for their favorite designs. From the entries receiving the most votes, the company selects winning designs, puts them into production, and gives prizes and royalties to the winning designers. In this way, the company harnesses the collective intelligence of a community of over 500,000 people to design and select T-shirts.
These examples of Web enabled collective intelligence are inspiring to read about. But to take advantage of the new possibilities they represent, it’s necessary to go beyond just seeing the examples as a fuzzy collection of “cool” ideas. To unlock the potential of collective intelligence, managers instead need a deeper understanding of how these systems work.
In this article we offer a new framework to help provide that understanding. It identifies the underlying building blocks—to use a biological metaphor, the “genes”—that are at the heart of collective intelligence systems, the conditions under which each gene is useful, and the possibilities for combining and re-combining these genes to harness crowds effectively.