Filmmaker, Video Editor, Motion Graphics Designer, and Photographer in Cairo, Egypt.
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Saturday, June 6, 2009

Understanding Collective Intelligence



Newer web applications trust their users, invite them to interact, connect them with others, gain early feedback from them, and then use the collected information to constantly improve the application.

Users are expressing themselves. This expression may be in the form of sharing their opinions on a product or a service through reviews or comments; through sharing and tagging content; through participation in an online community; or by contributing new content.

This increased user interaction and participation gives rise to data that can be converted into intelligence in your application. The use of collective intelligence to personalize a site for a user, to aid him in searching and making decisions, and to make the application more sticky are cherished goals that web applications try to fulfill.

More formally, collective intelligence (CI) simply and concisely means To effectively use the information provided by others to improve one’s application.

What is collective intelligence?
When a group of individuals collaborate or compete with each other, intelligence or behavior that otherwise didn’t exist suddenly emerges; this is commonly known as collective intelligence. The actions or influence of a few individuals slowly spread across the community until the actions become the norm for the community.

Example: The Hundredth Monkey Theory
In his book The Hundredth Monkey, Ken Keyes recounts an interesting story about how change is propagated in groups. In 1952, on the isolated Japanese island of Koshima, scientists observed a group of monkeys. They offered them sweet potatoes; the monkeys liked the sweet potatoes but found the taste of dirt and sand on the potatoes unpleasant.

One day, an 18-month-old monkey found a solution to the problem by washing the potato in a nearby stream of water. She taught this trick to her mother. Her playmates also learned the trick and taught it to their mothers.

Initially, only adults who imitated their children learned the new trick, while the others continued eating the old way. In the autumn of 1958, a number of monkeys were washing their potatoes before eating. The exact number is unknown, but let’s say that out of 1,000, there were 99 monkeys who washed their potatoes before eating.

Early one sunny morning, a 100th monkey decided to wash his potato. Then, incredibly, by evening all monkeys were washing their potatoes. The 100th monkey was that tipping point that caused others to change their habits for the better. Soon it was observed that monkeys on other islands were also washing their potatoes before eating them.

As users interact on the web and express their opinions, they influence others. Their initial circle of influence is the group of individuals that they most interact with. Because the web is a highly connected network of sites, this circle of influence grows and may shape the thoughts of everybody in the group. This circle of influence also grows rapidly throughout the community




Example: YouTube
In October 2006, Google bought YouTube for $1.65 billion. In its 20 months of existence, YouTube had grown to be one of the busiest sites on the Internet, dishing out 100 million video (As of September 2006) views a day. It ramped from zero to more than 20 million unique user visits a day, with mainly viral marketing—spread from person to person.

In YouTube’s case, each time a user uploaded a new video, she was easily able to invite others to view this video. As those others viewed this video, other related videos popped up as recommendations, keeping the user further engaged. Ultimately, many of these viewers also became submitters and uploaded their own videos as well. As the number of videos increased, the site became more and more attractive for new users to visit.

Harnessing information from users improves the perceived value of the application to both current and prospective users. This improved value will not only encourage current users to interact more, but will also attract new users to the application. The value of the application further improves as new users interact with it and contribute more content. This forms a self-reinforcing feedback loop, commonly known as a network effect, which enables wider adoption of the service.

References:
Satnam Alag, “Collective Intelligence In Action”, Manning Publications Co., first edition, 2009.

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