Aggregating qualitative assessments in multi-criteria decision-making

Link al seminario: https://loyola.webex.com/meet/rede3c

Abstract: Many decision-making problems involve the use of linguistic information collected by questionnaires based on ordered qualitative scales (OQSs). In such cases it is relevant how agents perceive the scales. Some of them can be considered as non-uniform, in the sense that agents may perceive different psychological proximities between consecutive terms of the scale. For instance, in the framework of health-care and medicine, the OQS {poor, fair, good, very good, excellent}, used by patients to evaluate self-rated health, it could be considered as non-uniform if ‘fair’ is perceived closer to ‘good’ than to ‘poor’, or if ‘good’ is perceived closer to ‘very good’ than to ‘fair’, or if ‘very good’ is perceived closer to ‘good’ than to ‘excellent’. To manage those perceptions, in 2015 we introduced the notion of ordinal proximity measure (OPM). It properly represents the proximities between the linguistic terms of OQSs in a pure ordinal way.

In this contribution, we summarize the multi-criteria decision-making procedure introduced in Applied Soft Computing 106, 107279 (2021). We consider that a group of panelists evaluate several alternatives regarding different criteria, each one through a specific OQS. These OQSs are equipped with OPMs that collect the perceptions about the proximities between the terms of the scales by means of ordinal degrees of proximity. The weights assigned to the criteria are managed in an ordinal way by replicating the qualitative assessments obtained for each alternative in each criterion as many times as necessary until these replications reflect the proportions among weights. The qualitative assessments provided by panelists are compared with the highest terms of the corresponding OQSs. To aggregate the obtained ordinal degrees of proximity, a homogenization process is provided.

Asistencia presencial: Aula E1-1-01 Campus de Córdoba