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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.

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Computational cost of the alphametic problem and of four random cryptarithmetic problems.The mean rescaled computational cost for the alphametic problem  (red circles) and for four ten-letter random cryptarithmetic problems with a unique solution (blue inverted triangles, magenta squares, cyan diamonds and green triangles). The symbols represent the mean rescaled computational cost  for a system composed of  fully connected agents. The independent variable  is the imitation probability. 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-g008: Computational cost of the alphametic problem and of four random cryptarithmetic problems.The mean rescaled computational cost for the alphametic problem (red circles) and for four ten-letter random cryptarithmetic problems with a unique solution (blue inverted triangles, magenta squares, cyan diamonds and green triangles). The symbols represent the mean rescaled computational cost for a system composed of fully connected agents. The independent variable is the imitation probability. 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: In order to verify the generality of our findings, which were obtained for the specific alphametic problem , we have considered a variety of random cryptarithmetic problems with 10 letters and a unique solution, so that the sizes of their solution spaces are the same as that of the alphametic problem. The comparison between the mean computational costs to solve four such random problems and our alphametic problem is shown in Figure 8 for the fully connected system. The results are qualitatively the same, as expected. The alphametic problem, however, was somewhat easier to solve by the cooperative system than the random problems, perhaps because of the coincidence of the last three letters (“ALD”) in the first and second operands. Interestingly, the independent system () cannot distinguish between the problems but the cooperative system () can, and this distinction is most pronounced when the parameters are set so as to achieve the optimal performance. It is as if the cooperative system had adapted to the specific task posed to it. We expect that our conclusions remain valid, in a qualitative sense of course, for any constraint satisfaction problem characterized by a rugged cost landscape.


Imitative learning as a connector of collective brains.

Fontanari JF - PLoS ONE (2014)

Computational cost of the alphametic problem and of four random cryptarithmetic problems.The mean rescaled computational cost for the alphametic problem  (red circles) and for four ten-letter random cryptarithmetic problems with a unique solution (blue inverted triangles, magenta squares, cyan diamonds and green triangles). The symbols represent the mean rescaled computational cost  for a system composed of  fully connected agents. The independent variable  is the imitation probability. 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-g008: Computational cost of the alphametic problem and of four random cryptarithmetic problems.The mean rescaled computational cost for the alphametic problem (red circles) and for four ten-letter random cryptarithmetic problems with a unique solution (blue inverted triangles, magenta squares, cyan diamonds and green triangles). The symbols represent the mean rescaled computational cost for a system composed of fully connected agents. The independent variable is the imitation probability. 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: In order to verify the generality of our findings, which were obtained for the specific alphametic problem , we have considered a variety of random cryptarithmetic problems with 10 letters and a unique solution, so that the sizes of their solution spaces are the same as that of the alphametic problem. The comparison between the mean computational costs to solve four such random problems and our alphametic problem is shown in Figure 8 for the fully connected system. The results are qualitatively the same, as expected. The alphametic problem, however, was somewhat easier to solve by the cooperative system than the random problems, perhaps because of the coincidence of the last three letters (“ALD”) in the first and second operands. Interestingly, the independent system () cannot distinguish between the problems but the cooperative system () can, and this distinction is most pronounced when the parameters are set so as to achieve the optimal performance. It is as if the cooperative system had adapted to the specific task posed to it. We expect that our conclusions remain valid, in a qualitative sense of course, for any constraint satisfaction problem characterized by a rugged cost landscape.

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