Limits...
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
Deviation from the exponential distribution for a large imitation probability.Probability distribution  of the rescaled computational cost for a search employing  fully connected agents with imitation probability . The mean of this distribution is . The solid straight line is the fitting function  with  and  in the regime of large cost. The influence network size is . The distribution was generated using  independent searches.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4199724&req=5

pone-0110517-g003: Deviation from the exponential distribution for a large imitation probability.Probability distribution of the rescaled computational cost for a search employing fully connected agents with imitation probability . The mean of this distribution is . The solid straight line is the fitting function with and in the regime of large cost. The influence network size is . The distribution was generated using independent searches.

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).


Imitative learning as a connector of collective brains.

Fontanari JF - PLoS ONE (2014)

Deviation from the exponential distribution for a large imitation probability.Probability distribution  of the rescaled computational cost for a search employing  fully connected agents with imitation probability . The mean of this distribution is . The solid straight line is the fitting function  with  and  in the regime of large cost. The influence network size is . The distribution was generated using  independent searches.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110517-g003: Deviation from the exponential distribution for a large imitation probability.Probability distribution of the rescaled computational cost for a search employing fully connected agents with imitation probability . The mean of this distribution is . The solid straight line is the fitting function with and in the regime of large cost. The influence network size is . The distribution was generated using independent searches.
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).

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