A recent research study at Stevens Institute of Technology suggests that an overabundance of information might impede the decision-making process.
When faced with challenging decisions, people often tend to gather a large amount of information quickly. However, according to a new study published in the blog “Cognitive Research: Principles and Implications,” this behavior could be counterproductive as having too much information can hinder, rather than improve, the quality of decision-making.
The lead author of the study, Associate Professor Samantha Kleinberg, who holds the Farber Chair at Stevens Institute of Technology, pointed out the paradox where individuals believe they are effectively using information to make good decisions, but in reality, an excess of information may not always be beneficial.
Contrasting Real-World Complexity with Simplified Models
In academic research, scholars often create simplified diagrams or models to demonstrate how various factors interact to produce specific results, with the aim of studying human decision-making processes.
While people can reason effectively about theoretical scenarios, such as animal interactions at a dance event, where they are free from biases or preconceptions, their ability to make well-informed decisions significantly decreases when faced with everyday situations like choosing the best dietary options.
Kleinberg’s study indicates that individuals’ existing knowledge and beliefs can lead them astray from the presented causal model, making it challenging for them to utilize the information effectively. This deviation can hinder decision-making, especially when personal beliefs clash with the information provided.
The Dilemma of Everyday Decision-Making
Expanding on their prior research conducted in 2020, Kleinberg and her co-author Jessecae Marsh, a cognitive neuroscientist at Lehigh University, carried out a series of experiments to explore how women’s decision-making processes are impacted by different causal models across a range of real-life subjects, including property investments, weight management, college selections, and voter engagement.
The experiments conducted at Lehigh University demonstrated that individuals can effectively use causal models, but even a slight surplus of information beyond what is necessary for informed decision-making can quickly render a basic model ineffective. Surprisingly, adding minimal extra information can severely impede decision-making, almost to the extent of making decisions without any information at all.
Kleinberg highlights that the challenge lies not just in the quantity of information but also in individuals’ difficulty in identifying the crucial components within the model that require attention.
Implications for Public Health and Beyond
This study has significant implications for public health and related fields, emphasizing the need to simplify educational messages to their core elements for effective communication. Kleinberg emphasizes that bombarding individuals with exhaustive lists of considerations, such as mask-wearing, COVID testing, or dietary options, can hinder rather than facilitate wise decision-making.
Interestingly, participants who were provided with less information consistently made better decisions, even when given the choice to request more or less information. Kleinberg suggests that streamlining and targeting causal models are essential for enabling individuals to make informed choices effectively.
One potential solution to enhance decision-making processes could involve the integration of AI chatbots tailored to offer personalized health guidance. By inputting intricate causal models into AI systems capable of identifying and highlighting the most relevant information for each individual, customized health advice could significantly enhance decision-making outcomes.
