Checking the consistency of the solution in ordinal semi-democratic decision-making problems

Abstract An interesting decision-making problem is that aggregating multi-agent preference orderings into a consensus ordering, in the case the agents׳ importance is expressed in the form of a rank-ordering. Due to the specificity of the problem, the scientific literature encompasses a relatively small number of aggregation techniques. For the aggregation to be effective, it is important that the consensus ordering well reflects the input data, i.e., the agents׳ preference orderings and importance rank-ordering. The aim of this paper is introducing a new quantitative tool – represented by the so-called p indicators – which allows to check the degree of consistency between consensus ordering and input data, from several perspectives. This tool is independent from the aggregation technique in use and applicable to a wide variety of practical contexts, e.g., problems in which preference orderings include omissions and/or incomparabilities between some alternatives. Also, the p indicators are simple, intuitive and practical for comparing the results obtained from different techniques. The description is supported by various application examples.

[1]  Fairouz Kamareddine,et al.  Logical Reasoning: A First Course , 2004 .

[2]  Franz-Josef Brandenburg,et al.  Comparing and Aggregating Partial Orders with Kendall tau Distances , 2012, Discret. Math. Algorithms Appl..

[3]  Ronald R. Yager,et al.  Fusion of multi-agent preference orderings , 2001, Fuzzy Sets Syst..

[4]  Paul B. Kantor,et al.  Predicting the effectiveness of naïve data fusion on the basis of system characteristics , 2000, J. Am. Soc. Inf. Sci..

[5]  C. Spearman The proof and measurement of association between two things. , 2015, International journal of epidemiology.

[6]  Wade D. Cook,et al.  Distance-based and ad hoc consensus models in ordinal preference ranking , 2006, Eur. J. Oper. Res..

[7]  Chirag N. Paunwala,et al.  Improved weight assignment approach for multimodal fusion , 2014, 2014 International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA).

[8]  Fiorenzo Franceschini,et al.  A novel algorithm for fusing preference orderings by rank-ordered agents , 2015, Fuzzy Sets Syst..

[9]  Alfred De Grazia,et al.  Mathematical Derivation of an Election System , 1953 .

[10]  Ching-Lai Hwang,et al.  Group decision making under multiple criteria , 1987 .

[11]  John D. Lafferty,et al.  Cranking: Combining Rankings Using Conditional Probability Models on Permutations , 2002, ICML.

[12]  Wang Jian-qiang Fusion of multiagent preference orderings with information on agent's importance being incomplete certain , 2007 .

[13]  Fiorenzo Franceschini,et al.  Management by Measurement: Designing Key Indicators and Performance Measurement Systems , 2007 .

[14]  C. F. Kossack,et al.  Rank Correlation Methods , 1949 .

[15]  K. Fine,et al.  Social Choice and Individual Rankings II , 1974 .

[16]  Alan Jessop,et al.  IMP: A decision aid for multiattribute evaluation using imprecise weight estimates , 2014 .

[17]  Juan Carlos Augusto,et al.  Ordering based decision making - A survey , 2013, Inf. Fusion.

[18]  P.-C.-F. Daunou,et al.  Mémoire sur les élections au scrutin , 1803 .

[19]  M. Kendall,et al.  Rank Correlation Methods , 1949 .

[20]  Panos M. Pardalos,et al.  Handbook of Multicriteria Analysis , 2010 .

[21]  Baoli Wang,et al.  Determining decision makers’ weights in group ranking: a granular computing method , 2014, International Journal of Machine Learning and Cybernetics.

[22]  Jean Marc Martel,et al.  Deux Propositions d'aide multicritère à la décision de groupe , 2000 .

[23]  P. Fishburn SOCIAL CHOICE FUNCTIONS , 1974 .

[24]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[25]  Shengli Wu,et al.  Performance prediction of data fusion for information retrieval , 2006, Inf. Process. Manag..

[26]  A. Bilbao-Terol,et al.  Using TOPSIS for assessing the sustainability of government bond funds , 2014 .

[27]  Witold Pedrycz,et al.  A review of soft consensus models in a fuzzy environment , 2014, Inf. Fusion.

[28]  S. Greco,et al.  Multiple Criteria Hierarchy Process with ELECTRE and PROMETHEE , 2013 .