Ranking Aggregation Based Decision Method

The object of this research is ranking aggregation algorithms.This paper mainly discussed ranking aggregation based decision method.Firstly,the author discovered the potential problem behind traditional multiple attribute decision making.The author used a simple example to prove that the result of traditional MADM question can be manipulate by adjust the score.After that,this paper discussed several traditional ranking aggregation algorithm,including Borda count method,Borda-kind method,Schultz's method and Markov's method.In the meantime of defining these algorithms,the author implemented these algorithms with Python code,and applied these algorithms in the invented dataset,resulting in a different conclusion comparing with traditional MADM.Secondly,this paper proposed a brand new weighted ranking aggregation algorithm and applied it in the same invented dataset.Thirdly,this paper constructed a simulation to comparing these above algorithms.The experiment showed that in specific situation,the weighted ranking aggregation is slightly better than the others.Finally,the author applied these many ranking aggregation algorithm in a real dataset which called The Best University of China,showed a different result from the view of ranking aggregation.