In applied mathematics and decision making, the agggregated indices randomization method (AIRM) is a modification of a well-known aggregated indices method,[citation needed] targeting complex objects subjected to multi-criteria estimation under uncertainty. AIRM was first developed by the Russian naval applied mathematician Aleksey Krylov around 1908. The main advantage of AIRM over other variants of aggregated indices methods is its ability to cope with poor-quality input information.

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  • In applied mathematics and decision making, the agggregated indices randomization method (AIRM) is a modification of a well-known aggregated indices method,[citation needed] targeting complex objects subjected to multi-criteria estimation under uncertainty. AIRM was first developed by the Russian naval applied mathematician Aleksey Krylov around 1908. The main advantage of AIRM over other variants of aggregated indices methods is its ability to cope with poor-quality input information. It can use non-numeric (ordinal), non-exact (interval) and non-complete expert information to solve multi-criteria decision analysis (MCDM) problems. An exact and transparent mathematical foundation can assure the precision and fidelity of AIRM results.
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  • In applied mathematics and decision making, the agggregated indices randomization method (AIRM) is a modification of a well-known aggregated indices method,[citation needed] targeting complex objects subjected to multi-criteria estimation under uncertainty. AIRM was first developed by the Russian naval applied mathematician Aleksey Krylov around 1908. The main advantage of AIRM over other variants of aggregated indices methods is its ability to cope with poor-quality input information.
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  • Aggregated indices randomization method
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