Collective intelligence is defined as the ability of a group to solve more problems than its individual members. It is argued that the obstacles created by individual cognitive limits and the difficulty of coordination can be overcome by using a collective mental map (CMM). A CMM is defined as an external memory with shared read/write access that represents problem states, actions and preferences for actions. It can be formalized as a weighted, directed graph. The creation of a network of pheromone trails by ant colonies points us to some basic mechanisms of CMM development: averaging of individual preferences, amplification of weak links by positive feedback, and integration of specialized subnetworks through division of labor. Similar mechanisms can be used to transform the World-Wide Web into a CMM, by supplementing it with weighted links.
Two types of algorithms are explored:
1) The co-occurrence of links in web pages or user selections can be used to compute a matrix of link strengths, thus generalizing the technique of “collaborative filtering”;
2) Learning web rules extract information from a user’s sequential path through the web in order to change link strengths and create new links. The resulting weighted web can be used to facilitate problem solving by suggesting related links to the user, or, more powerfully, by supporting a software agent that discovers relevant documents through spreading activation.