Sailing the seas of social uncertainty: Predicting COVID-19 health behavior through wisdom of the crowd

COVID-19 immersed us in a sea of uncertainties, several social: Will people wear masks? Are they wearing them now? Will people vaccinate? We were curious how well the wisdom of the crowd could reduce these uncertainties. Across two studies, we surveyed 1,869 students at the University of Kansas on their likelihood of engaging in health-protective behavior, how likely they assumed others were to engage in that behavior, and their confidence in those estimates. We also asked them to predict how other students would respond and collected numeracy, discounting, and risk-taking propensity measures. We compared predictions from multiple wisdom of the crowd aggregation methods, including simple averaging, weighted averaging, and the surprisingly popular algorithm, which makes use of differences between self- and other-related beliefs. We found that weighting by confidence produced predictions that most closely approximated actual observed data for mask-wearing. However, surprisingly popular predictions also proved accurate. We will discuss the implications of these findings, particularly in the context of identifying the environments when different wisdom of the crowd algorithms will work better or worse, and the challenges in using wisdom of the crowd algorithms to predict human behavior.