Probability Judgment

Probability judgment research investigates how humans and artificial systems assign probabilities to events, aiming to understand both the accuracy and the systematic biases in these judgments. Current research focuses on comparing human probability judgments to those of large language models, revealing shared inconsistencies with probability theory and exploring the underlying cognitive and computational mechanisms, such as Bayesian inference. This work is significant for advancing our understanding of human reasoning, improving the reliability of AI systems, and potentially informing applications in areas like decision-making under uncertainty.