A Choquet integral is a subadditive or superadditive integral created by the French mathematician Gustave Choquet in 1953.
[1] It was initially used in statistical mechanics and potential theory,[2] but found its way into decision theory in the 1980s,[3] where it is used as a way of measuring the expected utility of an uncertain event.
It is applied specifically to membership functions and capacities.
In imprecise probability theory, the Choquet integral is also used to calculate the lower expectation induced by a 2-monotone lower probability, or the upper expectation induced by a 2-alternating upper probability.
Using the Choquet integral to denote the expected utility of belief functions measured with capacities is a way to reconcile the Ellsberg paradox and the Allais paradox.
, that is Then the Choquet integral of
is defined by: where the integrals on the right-hand side are the usual Riemann integral (the integrands are integrable because they are monotone in
In general the Choquet integral does not satisfy additivity.
is not a probability measure, it may hold that for some functions
The Choquet integral does satisfy the following properties.
denote a cumulative distribution function such that
Then this following formula is often referred to as Choquet Integral: where
The Choquet integral was applied in image processing, video processing and computer vision.
In behavioral decision theory, Amos Tversky and Daniel Kahneman use the Choquet integral and related methods in their formulation of cumulative prospect theory.