Imitative Learning as a Connector of Collective Brains

Fontanari, José F.

doi:10.1371/journal.pone.0110517

Cited by 32 publications

(69 citation statements)

References 22 publications

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“…The computational cost is defined as the product of the number of agents in the group and the number of attempted trials till some agent finds the global maximum. Our main conclusion, namely, that for a fixed probability of imitation p there is a value of group size that minimizes the computational cost corroborates the findings of a similar study in which the task was to solve a particular cryptarithmetic problem [13]. Hence our conjecture that the efficacy of imitative learning could be a factor deter- minant of the group size of social animals (see [1,30] for a discussion on the standard selective pressures on group size in nature).…”

Section: Discussionsupporting

confidence: 85%

“…. , M −1 distinct randomly picked agent (here we have focused on the fully connected network L = M − 1 only) then there is an optimal connectivity value that minimizes the computational cost [13]. It would be most interesting to understand how the network topology influences the performance of the group of imitative agents and how the optimal network topology correlates with the known animal social networks [31].…”

Section: Discussionmentioning

confidence: 99%

“…It would be most interesting to understand how the network topology influences the performance of the group of imitative agents and how the optimal network topology correlates with the known animal social networks [31]. The main aim of our contribution is to show that the existence of an optimal group size that maximizes performance for imitative learning [13] is insensitive to the choice of the fitness landscape, so it is likely a robust property of populations that use imitation as a cooperative strategy. Although we have focused on the effect of the parameter K, which roughly determines the ruggedness of the NK landscape, the parameter N (the length of the strings) also plays a relevant role on the search for the global maximum, besides the obvious role of fixing the size 2 N of the search space.…”

Section: Discussionmentioning

confidence: 99%

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Exploring NK fitness landscapes using imitative learning

Fontanari

2015

Eur. Phys. J. B

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The idea that a group of cooperating agents can solve problems more efficiently than when those agents work independently is hardly controversial, despite our obliviousness of the conditions that make cooperation a successful problem solving strategy. Here we investigate the performance of a group of agents in locating the global maxima of NK fitness landscapes with varying degrees of ruggedness. Cooperation is taken into account through imitative learning and the broadcasting of messages informing on the fitness of each agent. We find a trade-off between the group size and the frequency of imitation: for rugged landscapes, too much imitation or too large a group yield a performance poorer than that of independent agents. By decreasing the diversity of the group, imitative learning may lead to duplication of work and hence to a decrease of its effective size. However, when the parameters are set to optimal values the cooperative group substantially outperforms the independent agents.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Exploring NK fitness landscapes using imitative learning

Fontanari

2015

Eur. Phys. J. B

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“…Imitation of local models [25] rather than of a global model as done here may allow the system to escape more quickly from the metastable states which we conjecture are the responsible for the delay on finding the correct solution in this case. We refer the reader to [26] for recent progress in this direction.…”

Section: Discussionmentioning

confidence: 99%

Solving a cryptarithmetic problem using a social learning heuristic

Fontanari

2014

2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

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The premiss that a group of cooperating agentsa collective brain -can solve a problem more efficiently than the same group of agents working independently is widespread, despite the little quantitative groundwork to support it. Here we use extensive agent-based simulations to investigate the performance of a system of N agents in solving a cryptarithmetic problem. Cooperation is taken into account through imitative learning which allows information to pass from one agent to another. At each trial the agents can either perform individual trial-and-test operations to explore the solution space or copy cues from a model agent, i.e., the agent that exhibits the lowest cost solution at the trial. We find a trade-off between the number of trial-and-test operations and the number of imitation attempts: too much imitation results in a performance which is poorer than that exhibited by noncooperative agents. For the optimal balance between trial-and-test operations and imitation attempts we find a thirtyfold speedup of the mean time to find the correct solution with respect to the time taken by the noncooperative group. Most significantly, we find that increasing the number of agents N beyond a certain value can greatly harm the performance of the cooperative system which can then perform much worse than in the noncooperative case. Low diversity and the following of a bad leader are the culprits for the poor performance in this case.

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“…These policies are based on the quality of the partial solutions the agents offer to solve the task. To address this problem we use an agent-based model where the agents perform individual trial-and-test searches to probe a fitness landscape (exploration) and imitate a model agent -the best performing agent in their influence neighborhood at the trial (exploitation) [6][7][8]. We consider a scenario where the agents are fixed at the nodes of a random geometric graph [10] and can interact with each other if the distance between them is less than a prespecified threshold.…”

Section: Introductionmentioning

confidence: 99%

Policies for allocation of information in task-oriented groups: elitism and egalitarianism outperform welfarism

2019

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Communication or influence networks are probably the most controllable of all factors that are known to impact on the problem-solving capability of task-forces. In the case connections are costly, it is necessary to implement a policy to allocate them to the individuals. Here we use an agent-based model to study how distinct allocation policies affect the performance of a group of agents whose task is to find the global maxima of NK fitness landscapes. Agents cooperate by broadcasting messages informing on their fitness and use this information to imitate the fittest agent in their influence neighborhoods. The larger the influence neighborhood of an agent, the more links, and hence information, the agent receives. We find that the elitist policy in which agents with above-average fitness have their influence neighborhoods amplified, whereas agents with below-average fitness have theirs deflated, is optimal for smooth landscapes, provided the group size is not too small. For rugged landscapes, however, the elitist policy can perform very poorly for certain group sizes. In addition, we find that the egalitarian policy, in which the size of the influence neighborhood is the same for all agents, is optimal for both smooth and rugged landscapes in the case of small groups. The welfarist policy, in which the actions of the elitist policy are reversed, is always suboptimal, i.e., depending on the group size it is outperformed by either the elitist or the egalitarian policies.

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Imitative Learning as a Connector of Collective Brains

Cited by 32 publications

References 22 publications

Exploring NK fitness landscapes using imitative learning

Exploring NK fitness landscapes using imitative learning

Solving a cryptarithmetic problem using a social learning heuristic

Policies for allocation of information in task-oriented groups: elitism and egalitarianism outperform welfarism

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