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Imitative learning as a connector of collective brains.

Fontanari JF - PLoS ONE (2014)

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.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.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil.

ABSTRACT
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.

Show MeSH
The effect of group size on the computational cost of the fully connected system.The symbols represent the mean rescaled computational cost  for the imitation probability  (magenta circles),  (red diamonds),  (green squares),  (blue inverted triangles) and  (cyan triangles). The independent variable  is the number of agents in the system. The influence network size is . Each symbol represents an average over  searches and the lines are guides to the eye. The error bars are smaller than the size of the symbols.
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pone-0110517-g004: The effect of group size on the computational cost of the fully connected system.The symbols represent the mean rescaled computational cost for the imitation probability (magenta circles), (red diamonds), (green squares), (blue inverted triangles) and (cyan triangles). The independent variable is the number of agents in the system. The influence network size is . Each symbol represents an average over searches and the lines are guides to the eye. The error bars are smaller than the size of the symbols.

Mentions: The effect of increasing the number of agents for a fixed value of the imitation probability is summarized in Figure 4. The mean computational cost of the cooperative system exhibits a non-monotonic dependence on , except in the case of the independent search () when it takes on a constant value. The benefit of cooperation is seen in this figure by the initial decrease of the computational cost as the number of agents increases. However, for all we find that the presence of too many agents greatly harms the performance of the system and that for a fixed there exists an optimum value of that maximizes the search efficiency of the cooperative system. For instance, although not shown in the scale of Figure 4, the minimum computational cost for occurs at . The efficiency at this optimum, however, is not affected significantly by the choice of the parameters and . In other words, the costs corresponding to the minima shown in Figures 2 and 4 are not very sensitive to changes in and , respectively. In particular, for the parameter settings we have explored, the best efficiency is achieved for and and amounts to a thirtyfold speed up with respect to the independent search.


Imitative learning as a connector of collective brains.

Fontanari JF - PLoS ONE (2014)

The effect of group size on the computational cost of the fully connected system.The symbols represent the mean rescaled computational cost  for the imitation probability  (magenta circles),  (red diamonds),  (green squares),  (blue inverted triangles) and  (cyan triangles). The independent variable  is the number of agents in the system. The influence network size is . Each symbol represents an average over  searches and the lines are guides to the eye. The error bars are smaller than the size of the symbols.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4199724&req=5

pone-0110517-g004: The effect of group size on the computational cost of the fully connected system.The symbols represent the mean rescaled computational cost for the imitation probability (magenta circles), (red diamonds), (green squares), (blue inverted triangles) and (cyan triangles). The independent variable is the number of agents in the system. The influence network size is . Each symbol represents an average over searches and the lines are guides to the eye. The error bars are smaller than the size of the symbols.
Mentions: The effect of increasing the number of agents for a fixed value of the imitation probability is summarized in Figure 4. The mean computational cost of the cooperative system exhibits a non-monotonic dependence on , except in the case of the independent search () when it takes on a constant value. The benefit of cooperation is seen in this figure by the initial decrease of the computational cost as the number of agents increases. However, for all we find that the presence of too many agents greatly harms the performance of the system and that for a fixed there exists an optimum value of that maximizes the search efficiency of the cooperative system. For instance, although not shown in the scale of Figure 4, the minimum computational cost for occurs at . The efficiency at this optimum, however, is not affected significantly by the choice of the parameters and . In other words, the costs corresponding to the minima shown in Figures 2 and 4 are not very sensitive to changes in and , respectively. In particular, for the parameter settings we have explored, the best efficiency is achieved for and and amounts to a thirtyfold speed up with respect to the independent search.

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.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.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil.

ABSTRACT
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.

Show MeSH