Even minimal information overload can hinder effective decision-making, according to new research from the Stevens Institute of Technology.
When faced with difficult choices, people often instinctively seek out extensive information. However, a recent study published in the journal Cognitive research: principles and their implications This suggests that this may actually be a problem. This influx of facts and details tends to undermine rather than enhance the quality of decision-making.
“This is counterintuitive, because we all like to think we’re using information wisely to make wise decisions,” said the paper’s lead author, Stevens Institute of Technology. said Associate Professor Samantha Kleinberg of the Farber Professor, a computer scientist at . “But the reality is, when it comes to information, more is not always better.”
Simple models and real world scenarios
To study how people make decisions, researchers typically use simple diagrams that show how different factors logically interact to produce a particular outcome, i.e., causal models. Create a.
When describing an abstract hypothetical scenario, such as an alien showdown at a dance party, most people have no prejudices or preconceptions about alien dance parties, so they can reason effectively about such models. I can. People pay attention to the information they are given, so they can make the right decisions.
but Kleinberg’s work We find that people’s ability to reason effectively almost evaporates when it comes to everyday scenarios, such as thinking about how to make healthy decisions regarding nutrition.
“We believe that people’s prior knowledge and beliefs distract from the causal model in front of them,” Kleinberg explained. “For example, when you are thinking about what to eat, you may have all sorts of preconceptions about what is best to eat, which can make it difficult to use the information presented effectively. Masu.”
Challenges to daily decisions
To test that hypothesis Based on 2020 researchKleinberg and co-author cognitive psychologist Jesse Marsh. lehigh universityinvestigates how people’s decisions change when presented with different types of causal models across a wide range of real-world topics, from home buying and weight management to college selection and voter turnout. We conducted a series of experiments to . It quickly became clear that people knew how to use causal models, but even very simple models provided very little information beyond what was strictly necessary to make good decisions. When additional information is added to the mix, it quickly becomes almost useless.
“What’s really remarkable is that even a small amount of extra information can have a huge negative impact on our decision-making,” Kleinberg says. “If you have too much information, your decision-making can quickly become as bad as having no information at all.”
For example, if a causal model shows that eating salty food increases blood pressure, but also shows unrelated information such as that drinking water makes you feel less thirsty, it may help people stay healthy. It becomes much more difficult to make effective choices about the best way to proceed. But when Kleinberg’s team emphasizes salient causal information, people’s ability to make good decisions quickly returns.
“This shows that the problem is not only that people are overwhelmed by the sheer amount of information, but also that they have a hard time understanding which parts of the model to focus on. , which is important,” Kleinberg said.
Impact on public health and beyond
This research has important implications for fields such as public health. This is because it means that educational messages need to be boiled down to their most essential parts and carefully presented to have a positive impact. “Giving people a huge list of things to consider when deciding whether to wear a face mask, get tested for coronavirus, what to eat, what to drink. It actually makes it harder for them to make good decisions,” Kleinberg said.
Even when Kleinberg and Marsh gave participants the option of receiving more or less information, those who asked for more information made worse decisions than those who asked for less. Kleinberg says, “If you give people the opportunity to overthink things, even if they seek additional information, things go wrong.” To help people make good decisions, we need simple, carefully targeted causal models. is required.”
One approach to supporting decision-making is to use AI chatbots to personalize health information and nutritional advice on a case-by-case basis. This is basically feeding a complex causal model into an AI model and having it detect and highlight only the problems. Specific information most relevant to a specific individual.
References: “Less is more: Information needs, information wants, and what makes causal models useful,” by Samantha Kleinberg and Jessieke K. Marsh, August 30, 2023. Cognitive research: principles and their implications.
DOI: 10.1186/s41235-023-00509-7
This research was funded by the James S. McDonnell Foundation and the National Science Foundation.