  The idea that a group of cooperating agents can solve problems more
efficiently than when those agents work independently is hardly controversial,
despite our obliviousness of the conditions that make cooperation a successful
problem solving strategy. Here we investigate the performance of a group of
agents in locating the global maxima of NK fitness landscapes with varying
degrees of ruggedness. Cooperation is taken into account through imitative
learning and the broadcasting of messages informing on the fitness of each
agent. We find a trade-off between the group size and the frequency of
imitation: for rugged landscapes, too much imitation or too large a group yield
a performance poorer than that of independent agents. By decreasing the
diversity of the group, imitative learning may lead to duplication of work and
hence to a decrease of its effective size. However, when the parameters are set
to optimal values the cooperative group substantially outperforms the
independent agents.
