Mapping the Genome of Collective Intelligence

 

Professor Malone in this talk presented their recent work on understanding a wide variety of emerging examples of Collective Intelligence from the point of view other than just “cool” ideas. He introduced a new framework to help provide this understanding by identifying 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 gens to harness crowds effectively.

 

Professor Malone and his team had gathered nearly 250 cases examples of Web enabled collective intelligence, from which they identified a relatively small set of building blocks that are combined and recombined in various ways in different collective intelligence systems. They listed two pairs of related questions for each examples:

--Who is perfoming the task? Why are they doing it?

--What is being accomplished? How is it being done?

 

By answering one of the four questions, they defined 19 genes by which collective intelligence system are built liken to the gene from which the individual organisms develop. The full combination of the genes can be viewed as the “genome” of one collective intelligence system.

 

For the question “Who”, they differentiated and identified two basic genes by answering the question of who undertakes the activity in an organization. The first one is Hierarchy, by which they meant, like in traditional hierarchical organizations, someone in authority assigned the task to a particular person or a group. The second one is Crowd, where activities can be undertaken by anyone in a large group who chooses to do so.

 

For the question “Why”, they identified three basic why genes to cover the high level motivations. Money, being the first one, is the financial promise from the organization as motivator to the individuals. Love is also a important motivator since some people work for their own interest, enjoyment. Besides, another important motivator is Glory. For example, in some open source software communities, programmers are motivated to gain the recognition from peers.

 

For the question “What”, they concluded two basic genes as the high level task being done in each case of collective intelligence system. The first one is Create, it is the process of contribution, followed by the Decide, when the organization evaluate and select alternatives.

 

For the question “How” , as they mainly focused on how the crowds used in intelligence system, they pointed out four variations of the answers to how gene for crowds based on the answer to the question—“whether they made their contributions and decisions independently of each other”. The gene Collection occurs when items contributed by the crowds are created independently of each other, while the gene Collaboration happens when items are created strongly dependently of each other. The gene Group decision occurs when members of a crowd reach a decision for the group as a whole. Method genes used in this process are recognized as Voting, Consensus, Averaging and Prediction markets. The gene individual decision occurs when each member of the crowd makes the chose by each own while it is not necessarily hold for all. Method genes used in this process are recognized as Markets and Social Networks.

 

He then explained how these genes could be combined into genomes of complete intelligence system, Linux, for one example. Anyone in the crowd who wants to create new software modules can contribute their part. And one few get paid as money reward, others would take Love and Glory as their main motivations. The work is finally done by collaboration. For the Decide part, they reached a decision on which modules were included in next release by only a few participants motivated by Love and Glory. This part is done by hierarchy.

 

Professor Malone ended the speech by comparing his work to another similar work, which is done by Quinn. He said there was still much work to be done to identify all different genes for collective intelligence. Actually, I think this is an absolutely useful start, but later on in the future, two problems are needed to explain properly in order to achieve higher usability of this system. First one, based on the paper and the talk I did not see how the genes varied or organization reacted as the outside environment of the organization, since each one has its own existing situation. The second one is that, will this conclusion hold for different culture, or do we have the same gene tags as we have the crowds from different culture, different geographic location or across time bound looking back to time when the internet web started and looking into future how organization at that time works.