Combining Information from Multiple Forecasters: Inefficiency of Central Tendency

TL;DR

This paper demonstrates that common central tendency aggregators like means and medians are generally inefficient for combining forecasts, especially when forecasters use different information or predictions are noisy.

Contribution

It provides a theoretical analysis showing the inefficiency of central tendency aggregators in aggregating information from multiple forecasters.

Findings

Central tendency aggregators are asymptotically inefficient with accurate forecasters.

Inefficiency persists even with noisy, distorted predictions.

Common aggregators do not optimally combine forecasters' information.

Abstract

Even though the forecasting literature agrees that aggregating multiple predictions of some future outcome typically outperforms the individual predictions, there is no general consensus about the right way to do this. Most common aggregators are means, defined loosely as aggregators that always remain between the smallest and largest predictions. Examples include the arithmetic mean, trimmed means, median, mid-range, and many other measures of central tendency. If the forecasters use different information, the aggregator ideally combines their information into a consensus without losing or distorting any of it. An aggregator that achieves this is considered efficient. Unfortunately, our results show that if the forecasters use their information accurately, an aggregator that always remains strictly between the smallest and largest predictions is never efficient in practice. A similar result holds even if the ideal predictions are distorted with random error that is centered at zero. If these noisy predictions are aggregated with a similar notion of centrality, then, under some mild conditions, the aggregator is asymptotically inefficient.