Algorithm Aversion in Forecasting: A Behavioral Investigation

The research project is positioned within the field of behavioral decision making and focuses on the topic of algorithm aversion in forecasting decisions. Algorithm aversion describes the phenomenon that many individuals tend to rely on human forecasters and discard algorithms even though the latter are generally performing better. Previous research has shown the reasons for this to be manifold and has also discussed possible managerial interventions. However, it has not been examined so far whether the complexity of the forecast algorithm or the reputation of the algorithm developer/provider influence individual's decision to apply an algorithm. This is of particular relevance today as forecasts are becoming more complex in the wake of big data and machine learning technologies. Since forecasts employing such methods are expected to be more accurate than human forecasts and simple statistical forecasting models, failure to make use of them could be very costly for companies. Therefore, this study examines to what extent individuals trust algorithms along the two dimensions complexity and reputation of forecasts. In other words, how important is it for decision makers to understand an algorithm and are they influenced by the reputation of the
developer/provider of the algorithm. The study will consist of four treatments (full factorial design of high/low algorithm complexity and high/low developer/provider reputation) in which participants will be asked to choose between making an own forecast or following the algorithm. The monetary incentive will be based on forecast accuracy.