New Paper by Clifton Forlines et al: “Researchers have long known that aggregate estimations built from the collected opinions of a large group of people often outperform the estimations of individual experts. This phenomenon is generally described as the “Wisdom of Crowds”. This approach has shown promise with respect to the task of accurately forecasting future events. Previous research has demonstrated the value of utilizing meta-forecasts (forecasts about what others in the group will predict) when aggregating group predictions. In this paper, we describe an extension to meta-forecasting and demonstrate the value of modeling the familiarity among a population’s members (its social network) and applying this model to forecast aggregation. A pair of studies demonstrates the value of taking this model into account, and the described technique produces aggregate forecasts for future events that are significantly better than the standard Wisdom of Crowds approach as well as previous meta-forecasting techniques.”
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