Vector Space Semantic Models Predict Subjective Probability Judgments for Real-World Events


We examine how people judge the probabilities of real-world events, such as natural disasters in different countries. We find that the associations between the words and phrases that constitute these events, as assessed by vector space semantic models, strongly correlate with the probabilities assigned to these events by participants. Thus, for example, the semantic proximity of “earthquake” and “Japan” accurately predicts judgments regarding the probability of an earthquake in Japan. Our results suggest that the mechanisms and representations at play in language are also active in high-level domains, such as judgment and decision making, and that existing insights regarding these representations can be used to make precise, quantitative, a priori predictions regarding the probability estimates of individuals.

Back to Table of Contents