Heuristic modeling of expectation formation in a complex experimental information environment

Academic subjects made judgmental forecasts of a graphically presented time series in a laboratory experiment. Besides the past realizations of the time series itself, the only available information for the forecasting task was provided by leading series, i.e. indicators with a constant lead period of one. The number and the quality of the leading series were varied systematically between seven versions of the experiment resulting in different levels of information complexity. We present a heuristic that explains the subjects' average forecasting behavior better than the rational expectations hypothesis in all versions of the experiment. Furthermore, we find that the forecasting accuracy of the subjects increases with the number of reliable indicators but their efficiency declines with increasing complexity.