Collaborative Intelligence - Distinguishing Collective From Collaborative Intelligence

Distinguishing Collective From Collaborative Intelligence

In some disciplines, the definition of Collaborative Intelligence has much in common with Collective Intelligence, so it is useful to find distinguishing aspects.

The term collective intelligence originally encompassed both collective and collaborative intelligence, and many systems manifest attributes of both. Pierre Lévy coined the term “collective intelligence” in his book of that title, first published in French in 1994. Lévy defined “collective intelligence” to encompass both collective and collaborative intelligence: “a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and in the effective mobilization of skills…” Following publication of Lévy’s book, computer scientists adopted the term collective intelligence to denote an application within the more general area to which this term now applies in computer science. Specifically, an application that processes input from a large number of discrete responders to specific, generally quantitative, questions (e.g. what will the price of DRAM be next year?) Algorithms homogenize input, maintaining the traditional anonymity of survey responders to generate better-than-average predictions. Note that collective intelligence in general does not require anonymity as a defining attribute, as shown by examples such as Wikipedia.

Collaborative intelligence relates to whether prediction is defined as active, how heuristics are used, and whether analogs to developmental processes for facilitated variation enable systems to evolve non-randomly toward increased functional effectiveness. Recent dependency network studies suggest links between collective and collaborative intelligence. Partial correlation-based Dependency Networks, a new class of correlation-based networks have been shown to uncover hidden relationships between the nodes of the network. Research by Dror Y. Kenett and his Ph.D. supervisor Eshel Ben-Jacob uncovered hidden information about the underlying structure of the U.S. stock market that was not present in the standard correlation networks, and published their findings in 2011.