  Collective or group intelligence is manifested in the fact that a team of
cooperating agents can solve problems more efficiently than when those agents
work in isolation. Although cooperation is, in general, a successful problem
solving strategy, it is not clear whether it merely speeds up the time to find
the solution, or whether it alters qualitatively the statistical signature of
the search for the solution. Here we review and offer insights on two
agent-based models of distributed cooperative problem-solving systems, whose
task is to solve a cryptarithmetic puzzle. The first model is the imitative
learning search in which the agents exchange information on the quality of
their partial solutions to the puzzle and imitate the most successful agent in
the group. This scenario predicts a very poor performance in the case imitation
is too frequent or the group is too large, a phenomenon akin to Groupthink of
social psychology. The second model is the blackboard organization in which
agents read and post hints on a public blackboard. This brainstorming scenario
performs the best when there is a stringent limit to the amount of information
that is exhibited on the board. Both cooperative scenarios produce a
substantial speed up of the time to solve the puzzle as compared with the
situation where the agents work in isolation. The statistical signature of the
search, however, is the same as that of the independent search.
