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Previously, we proposed a topological-based swarming model in which agents probabilistically decide which other agents to interact with based on the proximity of positions of agents. Agents then average their direction with the directions of the agents they have interacted with. In this paper, we improve the model mentioned-above by taking the influence of similarity of directions into consideration,...
This paper investigates a social learning model, in which each individuals' neighbors are composed by two parts: one consists of those who have similar beliefs; the other consists of those picked up according to certain probabilities. Each individual updates her beliefs by combining the Bayesian posterior beliefs based on her private signals and weighted averages of the beliefs of her neighbors. It...
In this paper, we consider the problem that a group of agents aims to compute the average of individually estimated noisy parameters by sharing information among a random network of digital links. In this scenario, the average consensus seeking is involved in a two-step procedure. First, each agent estimates the local time-varying parameters individually, and then agents average their estimations...
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