From Noise to Bias : Overconfidence in New Product Forecasting

We study decision behavior in the selection, forecasting, and production for a new product. In a stylized behavioral model and five experiments across three populations, we generate new insight into when and why this combination of tasks can lead to overconfidence. We theorize that cognitive limitations lead to noisy interpretations of signal information. Because people are statistically naive, they directly use their noisy interpretation of the signal information as their forecast, thereby under accounting for the uncertainty that underlies it. This process leads to unbiased forecast errors when considering products in isolation, but leads to positively-biased forecasts for the products people choose to launch due to a selection effect. We show this selection-driven overconfidence can be sufficiently problematic that under certain conditions choosing the product randomly can actually yield higher profits than when individuals themselves choose the product to launch. We provide mechanism evidence by manipulating the interpretation noise through information complexity — showing that even when the information is equivalent from a Bayesian perspective, more complicated information leads to more noise, which in turn leads to more overconfidence in the chosen products. Finally, we leverage this insight to show that getting a second independent forecast for a chosen product can significantly mitigate the overconfidence problem, even when both individuals have the same information