# The Wisdom of a Kalman Crowd

## TL;DR

This paper explores applying the Kalman Filter to aggregate human judgments, demonstrating it can improve decision-making accuracy by combining noisy estimates from multiple individuals.

## Contribution

It introduces the novel idea of using the Kalman Filter on crowd estimates and shows its effectiveness over traditional methods in forecasting tasks.

## Key findings

- Kalman Filter improves forecast accuracy when applied to crowd estimates.
- The method outperforms the Contribution Weighted Model across all horizons.
- Survey data confirms the practical benefits of the approach.

## Abstract

The Kalman Filter has been called one of the greatest inventions in statistics during the 20th century. Its purpose is to measure the state of a system by processing the noisy data received from different electronic sensors. In comparison, a useful resource for managers in their effort to make the right decisions is the wisdom of crowds. This phenomenon allows managers to combine judgments by different employees to get estimates that are often more accurate and reliable than estimates, which managers produce alone. Since harnessing the collective intelligence of employees, and filtering signals from multiple noisy sensors appear related, we looked at the possibility of using the Kalman Filter on estimates by people. Our predictions suggest, and our findings based on the Survey of Professional Forecasters reveal, that the Kalman Filter can help managers solve their decision-making problems by giving them stronger signals before they choose. Indeed, when used on a subset of forecasters identified by the Contribution Weighted Model, the Kalman Filter beat that rule clearly, across all the forecasting horizons in the survey.