The wisdom of crowds versus the madness of crowds

Evaluating the Wisdom of Crowds

Estimating and forecasting are difficult tasks. This is true whether the activity requires the determination of uncertain future event outcomes, or whether the estimation effort is complex in itself and based on insufficient information. Consequently, such tasks are frequently assigned to experts. Surprisingly, recent research suggests that collectives of non-experts can outperform individual e...

The Multidimensional Wisdom of Crowds

Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important method for annotating large datasets. We present a method for estimating the underlying value (e.g. the class) of each image from (noisy) annotations provided by multiple annotators. Our method is based on a model of the image formation and annotation process. Each image has different characterist...

Harnessing the Wisdom of Crowds*

We empirically examine the impact of herding on the accuracy of consensus earnings forecasts from a crowd-based platform (Estimize.com). We monitor user information set by tracking user viewing activities and running experiments. We find that the more public information a user views, the less weight she puts on her private information. While this improves the accuracy of individual forecasts, i...

The “Wisdom of Crowds” Effect

The Wisdom of Polarized Crowds

As political polarization in the United States continues to rise, the question of whether polarized individuals can fruitfully cooperate becomes pressing. Although diversity of individual perspectives typically leads to superior team performance on complex tasks, strong political perspectives have been associated with conflict, misinformation and a reluctance to engage with people and perspecti...