An integrated approach to identify criteria interactions based on association rule and capacity in MCDA

Criteria interaction is an inevitable factor to be considered, especially in complex multiple criteria decision analysis (MCDA) problems. With the help of capacity, criteria interactions could be modeled in the framework of MCDA. However, we cannot accurately understand the polarity and degree of criteria interactions for an MCDA problem in advance. To overcome this problem, we propose an unsupervised approach to identify and model criteria interactions based on association rules and 2-additive capacity. First, association rules between criteria are obtained to determine the polarity of criteria interactions. Then, through solving an optimization model, the degree of criteria interactions is modeled by 2-additive capacity. With the merits of identifying understandable interactions between criteria, decision makers could deal with complex MCDA problems in consideration with criteria interactions. The applicability and advantages of this approach are demonstrated by an urban sustainability assessment. The empirical study shows that positive correlations and negative interactions can be detected among three pillars of sustainability. City manager should focus on the improvement of the indicators that are positively interacted, because these indicators account for a large weight in aggregating overall sustainability performance and such interaction is not in line with sustainable development.

Automated summary

This paper proposes an unsupervised approach to identify and model criteria interactions in multiple criteria decision analysis (MCDA) problems, using association rules and 2-additive capacity. The approach is demonstrated through an urban sustainability assessment, showing its applicability and advantages.