2012/11/01 by Fintan Costello, Costello, Fintan, Paul Watts +1
Decision Sciences · Neuroscience · #Applications (stat.AP) #Artificial Intelligence (cs.AI) #Data Analysis #Decision-Making and Behavioral Economics #FOS: Computer and information sciences #FOS: Physical sciences #Psychology of Moral and Emotional Judgment #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1211.0501
openalex publication_date 2012/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The systematic biases seen in people's probability judgments are typically\ntaken as evidence that people do not reason about probability using the rules\nof probability theory, but instead use heuristics which sometimes yield\nreasonable judgments and sometimes systematic biases. This view has had a major\nimpact in economics, law, medicine, and other fields; indeed, the idea that\npeople cannot reason with probabilities has become a widespread truism. We\npresent a simple alternative to this view, where people reason about\nprobability according to probability theory but are subject to random variation\nor noise in the reasoning process. In this account the effect of noise is\ncancelled for some probabilistic expressions: analysing data from two\nexperiments we find that, for these expressions, people's probability judgments\nare strikingly close to those required by probability theory. For other\nexpressions this account produces systematic deviations in probability\nestimates. These deviations explain four reliable biases in human probabilistic\nreasoning (conservatism, subadditivity, conjunction and disjunction fallacies).\nThese results suggest that people's probability judgments embody the rules of\nprobability theory, and that biases in those judgments are due to the effects\nof random noise.\n