Assuming (without loss of generality) an ascending order, this means that category c1 consists of the worst alternatives whereas ck includes the best (most preferred) ones.
For instance, a predefined specific set of categories is often used to classify industrial accidents (e.g., major, minor, etc.).
[10] The preference disaggregation approach refers to the analysis of the decision–maker's global judgments in order to specify the parameters of the criteria aggregation model that best fit the decision-maker's evaluations.
In the case of MCP, the decision–maker's global judgments are expressed by classifying a set of reference alternatives (training examples).
For example, the following linear program can be formulated in the context of a weighted average model V(xi) = w1xi1 + ... + wnxin with wj being the (non-negative) trade-off constant for criterion j (w1 + ... + wn = 1) and xij being the data for alternative i on criterion j: This linear programming formulation can be generalized in context of additive value functions.