Demand Forecasting Behavior: System Neglect and Change Detection

This research analyzes how individuals make forecasts based on time series data, and tests an intervention designed to improve forecasting performance. Using data from a controlled laboratory experiment, we find that forecasting behavior systematically deviates from normative predictions: Forecasters over-react to errors in relatively stable environments, but under-react to errors in relatively unstable environments. Surprisingly, the performance loss due to systematic judgment biases is larger in stable than in unstable environments. In a second study, we test an intervention designed to mitigate these biased reaction patterns. In order to reduce the salience of recent demand signals, and emphasize the environment generating theses signals, we require forecasters to prepare a forecast in other time-series before returning to their original time-series. This intervention improves forecasting performance.