Predicting the Impact of Health States on Well-being: Explanations and Remedies for Biased Judgments
Affective forecasting research has demonstrated that people overestimate the impact of health states on both their own happiness and other peoples’ happiness, resulting in a disparity between a healthy sample’s predictions and the actual well-being of people living with that health state. The aim of this thesis was to explore these judgments, examine proposed explanations for the bias, and test existing and new methods for improving the accuracy. Using questionnaires, respondents predicted the impact of health states on either their own or on other peoples’ well-being. No actual difference was found in the happiness of people living and not living with health states but both groups made biased forecasts, although the predictions of respondents living with health states were biased to a lesser extent. As an explanation for inaccurate forecasts, the confound between whether a judgment was made for self happiness or others’ happiness, and whether or not the person was living with a health state, was found not to account for the bias. However, focusing too much attention on the impact of the health state, known as the focusing illusion, was concluded to be a plausible explanation. Although existing methods intended to reduce the effect of the illusion did not diminish the bias, a new method which encouraged consideration of the emotional impact of an event successfully moderated predictions. Furthermore, the bias was reduced by encouraging contemplation of the wider range of well-being of people living with health states, suggesting that biased forecasts were caused by anchoring on an extreme case. Additionally, receiving information on the happiness of people living with health states reduced the bias, but had less of an effect when presented with health state information. Thus the practicality of this remedy would be diminished in situations where health state information could not be withheld. Practical suggestions for improving affective forecasts and directions for future research are discussed.
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