LAUSR

People try to make decisions that will improve their lives and make them happy, and to do so, they rely on affective forecasts–predictions about how future outcomes will make them feel. Decades of research suggest that people are poor at predicting how they will feel and that they commonly overestimate the impact that future events will have on their emotions. Recent work reveals considerable variability in forecasting accuracy. This investigation tested a model of affective forecasting that captures this variability in bias by differentiating emotional intensity, emotional frequency, and mood. Two field studies examined affective forecasting in college students receiving grades on a midterm exam (Study 1, N = 643), and U.S. citizens after the outcome of the 2016 presidential election (Study 2, N = 706). Consistent with the proposed model, participants were more accurate in forecasting the intensity of their emotion and less accurate in forecasting emotion frequency and mood. Overestimation of the effect of the event on mood increased over time since the event. Three experimental studies examined mechanisms that contribute to differential forecasting accuracy. Biases in forecasting intensity were caused by changes in perceived event importance; biases in forecasting frequency of emotion were caused by changes in the frequency of thinking about the event. This is the first direct evidence mapping out strengths and weaknesses for different types of affective forecasts and the factors that contribute to this pattern.