Imagine you have multiple explanations for a complex problem. How do you choose the best one? Well, it turns out that simplicity is a key factor in this decision-making process. Researchers have found that simplicity plays a crucial role in evaluating explanations by serving as a cue to different aspects of probability. For example, simplicity can help us gauge the inputs to Bayesian inference, which involves calculating the prior probabilities and likelihoods of explanations. Additionally, simplicity is also a direct cue to the outputs of Bayesian inference – the posterior probability of an explanation. Even after accounting for other factors, simplicity still has a significant impact on our estimates of posterior probability. Interestingly, simplicity not only affects the computation of probabilities but also influences how satisfying an explanation feels. This suggests that simplicity serves multiple purposes when it comes to approximating probabilities in uncertain and challenging situations. If you’re curious to learn more about the fascinating relationship between simplicity and probability evaluation, check out the full article!

Abstract
People often face the challenge of evaluating competing explanations. One approach is to assess the explanations’ relative probabilities–for example, applying Bayesian inference to compute their posterior probabilities. Another approach is to consider an explanation’s qualities or “virtues,” such as its relative simplicity (i.e., the number of unexplained causes it invokes). The current work investigates how these two approaches are related. Study 1 found that simplicity is used to infer the inputs to Bayesian inference (explanations’ priors and likelihoods). Studies 1 and 2 found that simplicity is also used as a direct cue to the outputs of Bayesian inference (the posterior probability of an explanation), such that simplicity affects estimates of posterior probability even after controlling for elicited (Study 1) or provided (Study 2) priors and likelihoods, with simplicity having a larger effect in Study 1, where posteriors are more uncertain and difficult to compute. Comparing Studies 1 and 2 also suggested that simplicity plays additional roles unrelated to approximating probabilities, as reflected in simplicity’s effect on how “satisfying” (vs. probable) an explanation is, which remained largely unaffected by the difficulty of computing posteriors. Together, these results suggest that the virtue of simplicity is used in multiple ways to approximate probabilities (i.e., serving as a cue to priors, likelihoods, and posteriors) when these probabilities are otherwise uncertain or difficult to compute, but that the influence of simplicity also goes beyond these roles.

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