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Imitative learning as a connector of collective brains
The notion that cooperation can aid a group of agents to solve problems more
efficiently than if those agents worked in isolation is prevalent, despite the
little quantitative groundwork to support it. Here we consider a primordial
form of cooperation -- imitative learning -- that allows an effective exchange
of information between agents, which are viewed as the processing units of a
social intelligence system or collective brain. In particular, we use
agent-based simulations to study the performance of a group of agents in
solving a cryptarithmetic problem. An agent can either perform local random
moves to explore the solution space of the problem or imitate a model agent --
the best performing agent in its influence network. There is a complex
trade-off between the number of agents N and the imitation probability p, and
for the optimal balance between these parameters we observe a thirtyfold
diminution in the computational cost to find the solution of the
cryptarithmetic problem as compared with the independent search. If those
parameters are chosen far from the optimal setting, however, then imitative
learning can impair greatly the performance of the group. The observed
maladaptation of imitative learning for large N offers an alternative
explanation for the group size of social animals
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