Imitative learning as a connector of collective brains.
Bottom Line:
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.
View Article:
PubMed Central - PubMed
Affiliation: Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil.
ABSTRACT
Show MeSH
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:
Results -
Collection
License getmorefigures.php?uid=PMC4199724&req=5
Mentions: In the region where the mean computational cost decreases monotonically with increasing (e.g., for ) we found that the probability distribution of the computational cost is well described by an exponential distribution, in the sense that the ratio between the standard deviation and the mean is always very close to 1. (We recall that this ratio equals 1 for an exponential distribution.) However, in the region where increases with increasing we found that in the low cost regime gives values significantly greater than those predicted by an exponential distribution, as illustrated in Figure 3, though those values are not greater than those obtained in the case of the independent search (see Figure 1). |
View Article: PubMed Central - PubMed
Affiliation: Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil.