University of Miami (UM) researchers have developed a computational model and the associated conditions for reaching consensus in a broad spectrum of situations. "The new model helps us understand the collective behavior of adaptive agents--people, sensors, databases, or abstract entities--by analyzing communication patterns that are characteristic of social networks," says UM professor Kamal Premaratne.
Basic queries addressed by the model include what constitutes a good way to model opinions, how these opinions are updated, and when consensus is reached. Moreover, the model features the ability to accommodate uncertainties tied to soft data in combination with hard data.
"Our study takes into account the difficulties associated with the unstructured nature of the network," says UM professor Manohar N. Murthi. "By using a new 'belief updating mechanism,' our work establishes the conditions under which agents can reach a consensus, even in the presence of these difficulties."
Previous studies involved consensus reached via a reliance on how agents' updated their beliefs, while Premaratne says the new research has consensus consistent with a reliable estimate of the ground truth. The model strengthens an agent's credibility if the consensus opinion is closer to the agent's opinion.
The researchers want to expand the model to include organized opinion clusters, in which each cluster of agents share similar opinions.
From University of Miami
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