In applied mathematics and decision making, the agggregated indices randomization method (AIRM) is a modification of a wellknown aggregated indices method, targeting complex objects subjected to multicriteria 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 poorquality input information.
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 In applied mathematics and decision making, the agggregated indices randomization method (AIRM) is a modification of a wellknown aggregated indices method, targeting complex objects subjected to multicriteria 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 poorquality input information. It can use nonnumeric (ordinal), nonexact (interval) and noncomplete expert information to solve multicriteria 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 wellknown aggregated indices method, targeting complex objects subjected to multicriteria 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 poorquality input information.

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 Aggregated indices randomization method

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