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Deriving maximal consensus (minimal standard deviation) based on subjective ranking values and their correlations compared to the final ranking
Leading article: 2019. May (MIAU No. 249.)
(Previous article: MIAU No. 248.)
Keywords: mathematical democracy, similarity analysis, anti-discriminative principle, each-object-can-have-the-same-evaluation-value
Decision making can be enforced in a unison way. Behind a seemingly unison decision (ranking of objects),
there could be existing a lot of different preferences of the particular decision makers.
Believes and/or lobby-like force fields can be led to these preferences. Unison decision can be realized based on a process
where each preference will be compared to the final ranking list of the affected objects in order to minimize the standard deviation
(or the maximum-minimum-distance) of the correlations between the final ranks and the ranks of each decision maker.
This kind of consensus should be declared in advance – it means, each decision makers should agree
that the mathematical consensus will be accepted forever. This way makes possible to be honest and to act transparently
before the represented persons. Even, the impacts of the particular decision makers could be the same or appropriate to their legal weights.
This kind of robot negotiation can be realized in Excel based on its Solver-module.
It is possible that the correlation sign is negative between the consensus-based ranking and the anti-discriminative ranking.
Parallel, a more lobby-based situation leads to more sameness among the consensus-oriented ranking positions.
Based on the symmetry concerning the term of the correlation, it is possible to realize positive correlation
between Y0-ranking and consensus-ranking, but the differences are relevant – furthermore.
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