Acceleration Neglect in Trend Extrapolations

Many decisions rely on intuitive predictions based on time series data showing a trend. For instance, the current upward trend in global temperatures might lead to specific predictions about the extent to which global temperatures will rise in the future, and these predictions might be used to inform judgments about the urgency with which climate change must be addressed. However, those predictions often need to be revised to incorporate the effects of unexpected events that might accelerate a trend (i.e., increase its rate of change), such as an unanticipated increase in CO2 emissions, or decelerate a trend (i.e., decrease its rate of change), such as an unanticipated reduction in CO2 emissions. In this work, we uncover a new cognitive bias by which people neglect how much a trend can accelerate (vs. decelerate) due to unexpected events. We explain this bias in terms of momentum theory and a naive understanding of physics. These findings have important implications for businesses and policymakers seeking to communicate information about topics such as climate change, stock market prices, or disease prevention. Public Significance Statement We document an asymmetry in how unexpected events are incorporated into trend extrapolations. People predict smaller trend accelerations (i.e., increases in the rate of change of a trend) after being informed of an event that acts in the same direction as a trend than trend decelerations (i.e., decreases in the rate of change of a trend) after being informed of an event that opposes the direction of the trend. This asymmetry affects people's expectations about the development of trends in various domains, including average global temperatures, stock and housing prices, and the spread of diseases.