Decision-Making Revisited

Welcome back. More than a decade ago, I blogged about my decision to leave academia for the federal government. I wrote, How are you at making decisions? I’m big on intuition (ask Myers or her mother, Briggs); but I’m kind of wishy-washy if I don’t have a mountain of information and the results of surveying at least half the people on the East Coast. (Decision-Making Time).

A recent study by researchers affiliated with Stevens Institute of Technology and Lehigh University suggests my decision-making needs refinement. They show that most people’s decision-making actually gets worse, not better, when they’re given additional facts and details.

Modeling Decision-Making
To study how people make decisions, researchers typically create causal models--diagrams that show how different factors interact to yield specific outcomes. For these, the modelers choose either the fullest accounting of the causal structure, which may overwhelm decision makers, or simplified, targeted versions that omit miscellaneous detail.

Example of a complex causal model diagram with all the bells and whistles for managing a healthy body weight (Fig. 1 from cognitiveresearchjournal.springeropen.com/articles/10.1186/s41235-023-00509-7).

Studying People’s Decision-Making
For the recent study, the researchers conducted a series of experiments to explore how people’s decision-making varies when they’re presented different kinds of causal models of real-life topics (e.g., buying a house, managing body weight). The experiments enlisted from 300 to 800 US adults from the online Prolific survey platform before screening.

They first tested how the level of detail in a causal model influences decision accuracy. With no model (no diagram) as the control, they compared models targeted specifically to each decision to more complex models. Although targeted models might confuse if information that participants expect to see is omitted, the more complete models are lacking if participants can’t determine what part of a model (what part of the diagram) to use or if they’re unable to ignore irrelevant information.

 Targeted causal model for donating to charity (Fig. 4 from cognitiveresearchjournal.springeropen.com/articles/10.1186/s41235-023-00509-7).

The researchers then tested whether directing people’s attention to the relevant paths within more complex models--highlighting the paths--would gain the benefits of targeted information.

Complete causal model for donating to charity highlighted with the targeted path (Fig. 4 from cognitiveresearchjournal.springeropen.com/articles/10.1186/s41235-023-00509-7).

Finding that providing targeted or highlighted-path diagrams that focus on the information needed for a decision can lead to better choices than either complex diagrams or no diagrams, they tested the boundaries of this effect. They found that adding even a small amount of information beyond that related to the targeted answer was detrimental.

Wrap Up
Well, I’m impressed with causal models. What’s more important, I understand that overloading decision-making with information is not the way to go. But I’m more than a bit shaky about developing the targeted causal model to begin with.

As I wrote in that long-ago blog post, Lining up the pros and cons with my wife, who is rarely decision-challenged, didn’t produce any clear answer. One never knows all the pros and cons, but in some cases, we weren’t even sure which was which.

Thanks for stopping by.

P.S.
Study of causal models in Cognitive Research: Principles and Implications: cognitiveresearchjournal.springeropen.com/articles/10.1186/s41235-023-00509-7
Article on study on EurekAlert! website: www.eurekalert.org/news-releases/1002995