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Stochastic dominance is a partial order between random variables. [1] [2] It is a form of stochastic ordering.The concept arises in decision theory and decision analysis in situations where one gamble (a probability distribution over possible outcomes, also known as prospects) can be ranked as superior to another gamble for a broad class of decision-makers.
Stochastic dominance relations are a family of stochastic orderings used in decision theory: [1] Zeroth-order stochastic dominance: A ≺ ( 0 ) B {\displaystyle A\prec _{(0)}B} if and only if A ≤ B {\displaystyle A\leq B} for all realizations of these random variables and A < B {\displaystyle A<B} for at least one realization.
The MLR is an important condition on the type distribution of agents in mechanism design and economics of information, where Paul Milgrom defined "favorableness" of signals (in terms of stochastic dominance) as a consequence of MLR. [4]
Ranking gambles by mean-preserving spreads is a special case of ranking gambles by second-order stochastic dominance – namely, the special case of equal means: If B is a mean-preserving spread of A, then A is second-order stochastically dominant over B; and the converse holds if A and B have equal means.
The term stochastic process first appeared in English in a 1934 paper by Joseph L. Doob. [1] For the term and a specific mathematical definition, Doob cited another 1934 paper, where the term stochastischer Prozeß was used in German by Aleksandr Khinchin, [22] [23] though the German term had been used earlier in 1931 by Andrey Kolmogorov. [24]
This requires a stochastic ordering on the lotteries. Several such orderings exist; the most common in social choice theory, in order of strength, are DD (deterministic dominance), BD (bilinear dominance), SD (stochastic dominance) and PC (pairwise-comparison dominance). See stochastic ordering for definitions and examples.
Stochastic-dominance Pareto-efficiency [ edit ] Bogomolnaia and Moulin [ 4 ] : 302–303 present an efficiency notion for the setting of fair random assignment (where the bundle rankings are additive , the allocations are fractional , and the sum of fractions given to each agent must be at most 1 ).
Figure 1. 2D majorization example. For , , we have if and only if is in the convex hull of all vectors obtained by permuting the coordinates of .This is equivalent to saying that = for some doubly stochastic matrix. [2]: