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This paper aims to identify the potential conflicts that arise between the actual and the revealed preference of a panel of wine judges when the panel’s evaluation is derived by a linear aggregation of individual scores.

A standard axiomatic social choice theoretical model is used to derive and examine the findings.

The findings show that even with the application of a simple majority rule over the pairwise ranking of wines, preferences may be misrepresented by the ordinal ranking of the wine score aggregation.

A number of wine competitions and reviews, to date, use some form of linear aggregation to represent group preferences. Furthermore, tests surrounding wine judge performance are largely dependent on some underlying true measures usually derived from a linear aggregation. The results imply that care should be taken in these regards.

The purpose of this paper is to combine multidimensional welfare analysis and entropy metrics to derive not only the best relative weights but also substitution degree among different attributes to construct multidimensional indices of well-being with Chinese Household Income Project Survey 2002 data.

The authors follow Maasoumi’s two-step measures of multivariate inequality to calculate the inequality for three social groups in China, urban residents, migrants, and rural residents. The two-step approach provides an aggregation formula which is numerically identified in this paper based on a metric entropy distance measure between the distribution of the aggregate well-being functions, on the one hand, and the distribution of the self-reported “happiness” indicator. The authors compare the differences in relative weights and substitution degree for the three groups, and link them to some institutional factors.

The authors find that incorporating substitution among attributes, and taking into consideration group heterogeneity are very important in multidimensional analysis of well-being.

The two-step approach provides an aggregation formula which is numerically identified in this paper based on a metric entropy distance measure between the distribution of the aggregate well-being functions, on the one hand, and the distribution of the self-reported “happiness” indicator.