It's Complicated: Improving Decisions on Causally Complex Topics

We make frequent decisions about how to manage our health, yet do so with information that is highly complex or received piecemeal. Causal models can provide guidance about how components of a complex system interact, yet models that provide a complete causal story may be more complex than people can reason about. Prior work has provided mixed insights into our ability to make decisions with causal models, showing that people can use them in novel domains but that they may impede decisions in familiar ones. We examine how tailoring causal information to the question at hand may aid decision making, using simple diagrams with only the relevant causal paths (Experiment 1) or those paths highlighted within a complex causal model (Experiment 2). We find that diagrams tailored to a choice improve decision accuracy over complex diagrams or prior knowledge, providing new evidence for how causal models can aid decisions

Perceptions of Explanation Completeness Help Decrease Knowledge Overestimation

The tendency to overestimate one’s knowledge has been shown in many domains including the innerworkings of everyday objects. This Illusion of Explanatory Depth (IOED) can be broken through the act of generating a causal explanation, although the reason as to why has yet to be explored. In this study, we investigate what characteristics of a generated explanation result in people recognizing their perceived lack of knowledge. Participants completed a typical IOED paradigm for devices, followed by rating their perceived completeness and accuracy for the explanations they generated. We also coded the explanations to determine their causal complexity. We found that lower ratings of overall perceived completeness and a sense of incomplete big explanatory components were predictive of a larger decrease in perceived understanding for that device post-explanation. Fewer causal links within an explanation also predicted a larger decrease in understanding ratings, suggesting that producing an explanation with a lower causal complexity led to a decrease in perceived understanding of that device. We discuss the implications of these results in relation to explanation characteristics that may cause a person’s illusion of understanding to break and proposed origins of the IOED phenomenon