Imagine trying to predict how long a roller coaster ride will last without ever having been on one before. It sounds nearly impossible, right? Well, that’s similar to the challenge people faced when predicting the duration of an ongoing epidemic caused by a new virus. In a recent study, researchers investigated how well individuals could forecast the remaining duration of the COVID-19 epidemic in China, despite having minimal experience with such scenarios. What they found was fascinating! Rather than relying on complex calculations or previous knowledge, participants used simple similarity-based generalization to make their predictions. They essentially looked for patterns in recent history and projected them into the future. This cognitive algorithm allowed them to make reasonable predictions based on publicly available information. However, their forecasts were also influenced by negative emotions and their perception of time. Interestingly, when comparing participants with different levels of experience, the researchers discovered that those with more experience made different prediction behaviors compared to those with minimal experience. This suggests that prior familiarity can significantly impact our ability to predict unpredictable events. So the next time you’re faced with an unfamiliar situation, remember to take a step back and look for patterns in the past. Who knows, you might just become a prediction wizard!

Abstract
People are known for good predictions in domains they have rich experience with, such as everyday statistics and intuitive physics. But how well can they predict for problems they lack experience with, such as the duration of an ongoing epidemic caused by a new virus? Amid the first wave of COVID-19 in China, we conducted an online diary study, asking each of over 400 participants to predict the remaining duration of the epidemic, once per day for 14 days. Participants’ predictions reflected a reasonable use of publicly available information but were meanwhile biased, subject to the influence of negative affect and future time perspectives. Computational modeling revealed that participants neither relied on prior distributions of epidemic durations as in inferring everyday statistics, nor on mechanistic simulations of epidemic dynamics as in computing intuitive physics. Instead, with minimal experience, participants’ predictions were best explained by similarity-based generalization of the temporal pattern of epidemic statistics. In two control experiments, we further confirmed that such cognitive algorithm is not specific to the epidemic scenario and that minimal and rich experience do lead to different prediction behaviors for the same observations. We conclude that people generalize patterns in recent history to predict the future under minimal experience.

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