INDEXING SYSTEM

Evidence indicates that when people forecast potential social risks, they are guided not only by facts but often by motivated reasoning also. Here I apply a Bayesian decision framework to interpret the role of motivated reasoning during forecasting and assess some of the ensuing predictions. In 2 online studies, for each of a set of potential risky social events (e.g., economic crisis, rise of income inequality, and increase in violent crime), participants expressed judgments about the probability that the event will occur, how negative occurrence of the event would be, whether society is able to intervene in the event. Supporting predictions of the Bayesian decision model, the analyses revealed that participants who deemed the events as more prob-able also assessed occurrence of the events as more negative and believed society to be more capable to intervene in the events. Supporting the notion that a social threat is appraised as more probable when an intervention is deemed to be possible, these findings are compatible with a form of intervention bias. These observations are relevant for campaigns aimed at inform-ing the population about potential social risks such as climate change, economic dislocations, and pandemics.