When does more mean worse? Accuracy of judgmental forecasting is nonlinearly related to length of data series

Abstract

When people make forecasts from series of data, how does their accuracy depend on the length of the series? Previous research has produced highly conflicting findings: some work shows accuracy increases with more data; other research shows that it decreases. In two experiments, we found an inverted U-shaped relation between forecast error and series length for various series containing different patterns and noise levels: error decreased as the length of the series increased from five through 20 to 40 items but also decreased as the series length decreased from five through two to one item. We argue that, with short series, people use a simple heuristic approach to forecasting (e.g., the naïve forecast). With longer series, they extract patterns from the series and extrapolate from them to produce their forecasts. Use of heuristics is poorer but extraction of patterns is better when there are more items in the series. For series of intermediate length, neither type of strategy operates well, thereby producing the inverted U-shaped relation that we observed. Implications for unaided judgmental forecasting and for forecasting based on a combination of judgmental and statistical methods are discussed.

Forecasting: theory and practice

“…We have discussed only intuitive processes underlying judgmental forecasting (Gigerenzer, 2007). However, they can be supplemented by deliberative (System 2) processes (Theocharis and Harvey, 2019) in some circumstances.…”

Association of quantitative information and patient knowledge about prostate cancer outcomes.

“…Prior work suggests that the relationship between the amount of information and information utilization in decision making follows an inverted U -shape; that is, the quantity of information presented to an individual improves their understanding of a problem until the point when information processing capacity is reached, beyond which their understanding might decline (Chewning & Harrell, 1990; Eppler & Mengis, 2004; Roetzel, 2019; Theocharis & Harvey, 2019), Based on this work, we hypothesize that patients will better understand cancer outcomes until quantitative information reaches a certain amount after which their ability to answer questions assessing their knowledge of cancer outcomes might be reduced. Furthermore, previous literature has demonstrated that patient education level could influence how well a patient evaluates information (Ek, 2004).…”