Subadditivity effect

The subadditivity effect is the tendency for people’s probability estimates of mutually exclusive possibilities to sum to more than their estimate for their union.

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Support theory: A theory of judgment in which probability estimates depend on how events are described and supported by evidence. It explains why partitioning an event into named possibilities can raise its summed estimate.
Linda problem: A probability-judgment problem in which people often rate a conjunction as more likely than one of its components. It demonstrates a related probability error, though its conjunction fallacy is distinct from subadditivity.
Additivity of probability: The rule that probabilities of mutually exclusive events sum to the probability of their union. Subadditivity names the departure from this normative rule in people’s estimates.
Amos Tversky: A psychologist whose research with Daniel Kahneman shaped modern theories of judgment and decision-making. His work on support theory helped explain how event descriptions alter probability estimates.
Unpacking effect: The increase in an estimated probability when an event is divided into explicitly named subevents. It is a direct route to subadditivity: listed possibilities receive more attention than an unlisted remainder.
Kahneman and Tversky's 1979 football study: An experiment showing that people’s estimates for specific football outcomes could exceed their estimate for a broader outcome containing them. It is a canonical demonstration of overestimation when an event is divided into specific possibilities.
Conjunction fallacy: The error of judging a conjunction of events more likely than one of its component events. It also violates probability constraints, but compares a conjunction with a component rather than partition estimates with a whole.
Daniel Kahneman: A psychologist whose research on judgment and decision-making earned the 2002 Nobel Memorial Prize in Economic Sciences. His studies with Tversky established influential demonstrations of probability judgment errors.
Availability heuristic: A judgment strategy that estimates frequency or probability by how readily examples come to mind. Vivid, separately recalled outcomes can inflate estimates for the parts of a partition.
Medical diagnosis probability judgments: Assessments of the likelihood that a patient has a disease given symptoms and test results. Listing diagnostic explanations separately can yield a larger total than judging their combined category.