Abstract
In this paper we analyze a class of multiagent consensus dynamical systems inspired by Krause's original model. As in Krause's model, the basic assumption is the so-called bounded confidence: two agents can influence each other only when their state values are below a given distance threshold $R$. We study the system under an Eulerian point of view considering (possibly continuous) probability distributions of agents, and we present original convergence results. The limit distribution is always necessarily a convex combination of delta functions at least $R$ far apart from each other: in other terms these models are locally aggregating. The Eulerian perspective provides the natural framework for designing a numerical algorithm, by which we obtain several simulations in $1$ and $2$ dimensions.
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