Having complete information in hand prior to dealing a situation is not necessarily an effective measure of decision making. In contrast to that, this new research suggests that too much knowledge can lead people to make worse decisions, and already known facts can get in the way of choosing the best outcome.
Latest research from Stevens Institute of Technology points about the critical gap in our understanding of how new information interacts with prior knowledge and beliefs.
The study led by Samantha Kleinberg, associate professor of Computer Science at Stevens, is helping reframe the idea of how we use the mountain of data extracted from artificial intelligence and machine learning algorithms and how healthcare professionals and financial advisors present this new information to their patients and clients.
According to the team, the basic point in the study that they have identified is the need to help people build upon what they already know and understand how they will use the new information.
Kleinberg and colleagues asked 4,000 participants a series of questions about topics with which they would have varying degrees of familiarity. Some were asked questions about situations that they are already familiar with, whereas some were thrown completely to a new space, providing giving new information related to the situation.
Upon the experiment, Kleinberg and her team, including former Stevens graduate student Min Zheng and cognitive scientist Jessecae Marsh from Lehigh University, found that when people make decisions in novel scenarios, they do very well on that problem.
"People are just focusing on what's in the problem, they are not adding in all this extra stuff," said Kleinberg
However, when that problem, with the same causal structure, was replaced with information about finances and retirement, for example, people became less confident in their choices and made worse decisions, suggesting that their prior knowledge got in the way of choosing the best outcome.
Concluding the study, the team emphasised that it is how we tailor information to the existing set of information or belief that one holds, yield the best result.
( With inputs from ANI )