Using Belief Functions to Forecast Demand for Mobile Satellite Services

Abstract. This paper outlines an application of belief functions to forecasting the demand for a new service in a new category, based on new technology. Forecasting demand for a new product or service is always diÆcult. It is more so when the product category itself is new, and so unfamiliar to potential consumers, and the quality of service of the product is dependent upon a new technology whose actual performance quality is not known in advance. In such a situation, market research is often unreliable, and so the beliefs of key stakeholders regarding the true values of underlying variables typically vary considerably. Belief functions provide a means of representing and combining these varied beliefs which is more expressive than traditional point probability estimates.

Registered replication report: Schooler and Engstler-Schooler (1990)

Trying to remember something now typically improves your ability to remember it later. However, after watching a video of a simulated bank robbery, participants who verbally described the robber were 25% worse at identifying the robber in a lineup than were participants who instead listed U.S. states and capitals—this has been termed the “verbal overshadowing” effect (Schooler and Engstler-Schooler, 1990). More recent studies suggested that this effect might be substantially smaller than first reported. Given uncertainty about the effect size, the influence of this finding in the memory literature, and its practical importance for police procedures, we conducted two collections of preregistered direct replications (RRR1 and RRR2) that differed only in the order of the description task and a filler task. In RRR1, when the description task immediately followed the robbery, participants who provided a description were 4% less likely to select the robber than were those in the control condition. In RRR2, when the description was delayed by 20 min, they were 16% less likely to select the robber. These findings reveal a robust verbal overshadowing effect that is strongly influenced by the relative timing of the tasks. The discussion considers further implications of these replications for our understanding of verbal overshadowing