When people have to make a tough decision, their first instinct is usually to gather as much information as possible. Just one problem: according to research published this week in Cognitive Research: Principles and Implications, most people's decision-making actually gets worse, not better, when you give them additional facts and details.
to picking a college and increasing voter turnout. It quickly became apparent that people know how to use causal models, but even a very simple model quickly becomes all but useless when just a little additional detail, beyond the information that's strictly necessary to make a good decision, is added to the mix.
"What's really remarkable is that even a tiny amount of surplus information has a big negative effect on our decision-making," said Kleinberg."If you get too much information, your decision-making quickly becomes as bad as if you'd gotten no information at all.", but also shows extraneous information such as drinking water makes you less thirsty, for instance, it becomes much harder for people to make effective choices about the best way to maintain their health.
"That's significant because it shows that the problem isn't just that people are overwhelmed by the sheer quantity of information—it's more that they're struggling to figure out which parts of the model they should be paying attention to," said Kleinberg.because it means that educational messages need to be simmered down to their most essential parts and carefully presented in order to have a positive impact.
Even when Kleinberg and Marsh gave participants the option of receiving more or less information, those who asked for more information made poorer decisions than those who asked for less."If you give people the opportunity to overthink, even when they ask for additional information," said Kleinberg,"things go poorly. People need simple and carefully targeted causal models in order to make good decisions.
One approach to aid decision-making might be to use AI chatbots to tailor health information or nutritional advice to individuals on a case-by-case basis—essentially feeding a complex causal model into the AI model, and letting it detect and highlight only the specific information that's most relevant to a particular individual.
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