Imitative learning as a connector of collective brains.
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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.If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group.
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PubMed Central - PubMed
Affiliation: Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil.
ABSTRACT
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The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent in computer science and business circles. 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 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. Related in: MedlinePlus |
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Mentions: In Figure 5 we present the probability distribution using searches for the imitation probability and two representative values of . Figure 5A shows this distribution for , which corresponds to a regime of low computational cost according to Figure 4. We observe a pronounced maximum at so that in most searches the model cost remains unaltered for 3 to 5 trials. This is an optimum scenario since no string stays on the top tier long enough to influence the entire system. For , we find that exhibits a similar shape but the maximum becomes sharper and its location is shifted towards lower values of as decreases. Figure 5B, which shows the results for , reveals a very different scenario: the distribution exhibits a plateau indicating that the model cost remains unchanged for hundreds to a few thousands trials. For very large values of , the distribution seems to exhibit an exponential decay to zero, namely, . We stress that for the two cases exhibited in Figure 5 the probability that an agent will imitate the model rather than perform an elementary move is the same, namely , and so the qualitative differences reported in the figure are due solely to the change on the number of agents. |
View Article: PubMed Central - PubMed
Affiliation: Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil.