Overview
Affective forecasting can be divided into four components: predictions about emotional valence (i.e. positive or negative), the specific emotions experienced, their duration, and their intensity. While errors may occur in all four components, research overwhelmingly indicates that the two areas most prone to bias, usually in the form of overestimation, are duration and intensity. Immune neglect is a form of impact bias in response to negative events whereby people fail to predict how much their psychological immune system will hasten their recovery. On average, people are fairly accurate about predicting which emotions they will feel in response to future events. However, some studies indicate that predicting specific emotions in response to more complex social events leads to greater inaccuracy. For example, one study found that while many women who imagine encountering gender harassment predict feelings of anger, in reality, a much higher proportion report feelings of fear. Other research suggests that accuracy in affective forecasting is greater for positive affect than negative affect, suggesting an overall tendency to overreact to perceived negative events. Gilbert and Wilson posit that this is a result of our psychological immune system.
While affective forecasts take place in the present moment, researchers also investigate its future outcomes. That is, they analyze forecasting as a two-step process, encompassing a current prediction as well as a future event. Breaking down the present and future stages allow researchers to measure accuracy, as well as tease out how errors occur. Gilbert and Wilson, for example, categorize errors based on which component they affect and when they enter the forecasting process. In the present phase of affective forecasting, forecasters bring to mind a mental representation of the future event and predict how they will respond emotionally to it. The future phase includes the initial emotional response to the onset of the event, as well as subsequent emotional outcomes, for example, the fading of the initial feeling.
When errors occur throughout the forecasting process, people are vulnerable to biases. These biases disable people from accurately predicting their future emotions. Errors may arise due to extrinsic factors, such as framing effects, or intrinsic ones, such as cognitive biases or expectation effects. Because accuracy is often measured as the discrepancy between a forecaster's present prediction and the eventual outcome, researchers also study how time affects affective forecasting. For example, the tendency for people to represent distant events differently from close events is captured in construal level theory.
The finding that people are generally inaccurate affective forecasters has been most obviously incorporated into conceptualizations of happiness and its successful pursuit, as well as decision-making across disciplines. Findings in affective forecasts have stimulated philosophical and ethical debates, for example, on how to define welfare. On an applied level, findings have informed various approaches to healthcare policy, tort law, consumer decision-making, and measuring utility (see below sections on psychology, economics, law, and health).
Newer and conflicting evidence suggests that intensity bias in affective forecasting may not be as strong as previous research indicates. Five studies, including a meta-analysis recovers evidence that overestimation in affective forecasting is partly due to the methodology of past research. Their results indicate that some participants misinterpreted specific questions in affective forecasting testing. For example, one study found that undergraduate students tended to overestimate experienced happiness levels when participants were asked how they were feeling in general with and without reference to the election, compared to when participants were asked how they were feeling specifically in reference to the election. Findings indicated that 75%-81% of participants asked general questions misinterpreted them. After clarification of tasks, participants were able to more accurately predict the intensity of their emotions
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