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How consensus forms on social networks

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How consensus forms on social networks

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Social networks are undoubtedly a major part of modern life, and an increasingly common aspect of organizational life.  There has been considerable discussion around the value of such networks however, particularly when it comes to forming opinions.  There have been a few studies recently that suggest that social networks do little to promote divergent thinking and ideas, thus often resulting in a kind of sanitized group think.  All of which isn’t so great for companies trying to be a bit more innovative.

It’s interesting to read, therefore, a new study that explores just how opinions tend to evolve on our social networks.  The researchers created a computational model to test their hypothesis around the kind of conditions that promote a consensus forming across our social networks.

“We wanted to provide a new method for studying the exchange of opinions and evidence in networks,” the researchers said. “The new model helps us understand the collective behavior of adaptive agents—people, sensors, data bases or abstract entities—by analyzing communication patterns that are characteristic of social networks.”

The researchers believe that their model will address some of the uncertainties associated with soft data, such as the opinions of people, in addition to that of harder data such as statistics and facts.  This will hopefully allow them to model both how opinions form and when a consensus is reached.

“Human-generated opinions are more nuanced than physical data and require rich models to capture them,” they say. “Our study takes into account the difficulties associated with the unstructured nature of the network,” they add. “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.”

The model sees nodes in the network exchanging and revising their beliefs via interaction with other nodes.  The interaction is usually local in context, with nodes exchanging information with neighbouring nodes.

Previous attempts at modelling consensus forming suggest that the key is on the processes nodes use to update their beliefs.  Different methods of updating ones information results in different consensus states.  The researchers hope that their own system is both more rational and meaningful.

“In our work, the consensus is consistent with a reliable estimate of the ground truth, if it is available,” they say. “This consistency is very important, because it allows us to estimate how credible each agent is.”

So, the model suggests that consensus tends to form when the consensus opinion is close to that of a credible node in the network.

“The fact that the same strategy can be used even in the absence of a ground truth is of immense importance because, in practice, we often have to determine if an agent is credible or not when we don’t have knowledge of the ground truth,” they say.

In the future, the researchers would like to expand their model to include the formation of opinion clusters, where each cluster of nodes share similar opinions. Clustering can be seen in the emergence of extremism, minority opinion spreading, the appearance of political affiliations, or affinity for a particular product, for example.

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