"We might expect that knowing more about a person would allow us to make better predictions of how they would react to events like their team winning a game or their political party winning or losing an election, but we found the opposite," said study author Mina Cikara of Harvard University.
Most of the decisions that we make in our daily lives, whether choosing sandwiches, vacations, or medical procedures, involve predictions about how much pleasure or pain we'll experience given various possible outcomes.
This kind of prediction, what researchers call an "affective forecast," also plays a critical role in the decisions we make for others.
"Doctors might try to gauge the amount of pain people are in when deciding the kinds of drugs to prescribe them, employers might consider how happy their employees would be made by a smaller or larger holiday bonus, and politicians may consider the pain and distress that people are experiencing when deciding whether to intervene in a humanitarian crisis," Cikara noted.
Considerable research has explored how people make these kinds of affective forecasts for themselves, but Cikara and collaborators wanted to know whether we make affective forecasts differently for other people.
In one online study, the researchers used data from the 2014 US Senate midterm elections to investigate.
The researchers found that people made predictions that were more extreme and less accurate when they knew the political affiliation of the person they were making predictions for.
The researchers observed similar results in the context of a college football game - the annual game between Harvard and Yale.
Students tailgating before the game overestimated how unhappy Yale fans would feel when their team lost. They were more accurate when they did not know whether someone was "Yale fan" or not.
