Abstract

Decision makers frequently have to forecast the future values of a time series (e.g. the price of a commodity, sales figures) given several sources of information (e.g. leading indicators, forecasts of advisors). As a subdomain of decision theory the explanation and the improvement of human forecasting behavior are interdisciplinary issues and have been subject to extensive empirical field and laboratory research. We here review the relevant experimental literature, demonstrate the significance of these results for decision science in general, and summarize the implications for practical forecasting applications.

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