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

Sandro M. Reia, Paulo F. Gomes and José F. Fontanari ()
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Sandro M. Reia: Instituto de Física de São Carlos, Universidade de São Paulo
Paulo F. Gomes: Instituto de Física de São Carlos, Universidade de São Paulo
José F. Fontanari: Instituto de Física de São Carlos, Universidade de São Paulo

The European Physical Journal B: Condensed Matter and Complex Systems, 2019, vol. 92, issue 9, 1-10

Abstract: Abstract 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. Graphical abstract

Keywords: Statistical; and; Nonlinear; Physics (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1140/epjb/e2019-100345-7

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