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handle: 10810/30664
Aggregation functions are mathematical operators that merge given data in order to obtain a global value that preserves the information given by the data as much as possible. In most practical applications, this value is expected to be between the infimum and the supremum of the given data, which is guaranteed only when the aggregation functions are idempotent. Ordered weighted averaging (OWA) operators are particular cases of this kind of function, with the particularity that the obtained global value depends on neither the source nor the expert that provides each datum, but only on the set of values. They have been classified by means of the orness—a measurement of the proximity of an OWA operator to the OR-operator. In this paper, the concept of orness is extended to the framework of idempotent aggregation functions defined both on the real unit interval and on a complete lattice with a local finiteness condition.
lattice operators, Management decision making, including multiple objectives, Orness, Idempotence, Decision theory, Reasoning under uncertainty in the context of artificial intelligence, QA1-939, orness, aggregation functions, idempotence, aggregation functions; lattice operators; idempotence; orness, Mathematics, Lattice operators, Aggregation functions
lattice operators, Management decision making, including multiple objectives, Orness, Idempotence, Decision theory, Reasoning under uncertainty in the context of artificial intelligence, QA1-939, orness, aggregation functions, idempotence, aggregation functions; lattice operators; idempotence; orness, Mathematics, Lattice operators, Aggregation functions
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