• What is the problem/issue being addressed?
To guarantee a smooth transition to a sustainable future, there is an impelling need for providing businesses, policy makers, and the general public with an understanding of the impacts and costs of goods and services. Due to the multitude of impacts and costs, there is a clear need for methods that can convey the overall performance of competing alternatives (e.g. different cars, energy technologies, and also policies). Multiple Criteria Decision Analysis (MCDA) methods are excellent tools that can be used to support these decision-making processes. Many MCDA methods are available, and there was not until now a Decision Support System (DSS) capable of leading a decision analyst in the complex process of selecting the appropriate method(s) for a specific decision-making problem. This was thus the central challenge tackled by this project.
• Why is it important for society?
The development of a DSS to recommend MCDA method(s) is of fundamental importance for a variety of reasons. Firstly, the appropriate method has to be chosen for each decision-making problem to guarantee that the provided decision recommendation is meaningful for the decision makers. Secondly, it is necessary to have a DSS that can help analysts prioritizing efforts for reducing knowledge gaps in the description of the decision-making problems. Thirdly, it is important to have a tool capable of unveiling methodological mistakes in selecting the methods to avoid such wrongdoings in future studies.
• What are the overall objectives?
This project formalizes and contextualizes the current MCDA methods leading to the development of a comprehensive DSS (called the MCDA Methods Selection Software, MCDA-MSS) that selects the most relevant MCDA method(s) for solving decision-making problems. The MCDA-MSS was tested in the areas of Alternatives Assessment (AA) (e.g. materials, products, and technologies assessment) to assess its performance, intelligibility, and updatability.
• What are the conclusions of the action?
This research has confirmed that there is a tendency of mostly focusing on obtaining results from the application of the MCDA methods, rather than on justifying the process followed to select the chosen methods. This has resulted, at least with the literature analysed during this project, in a large share (just under 60%) of misuses of MCDA methods, which unlikely supported good and better decision-making. This finding suggests that decision analysts should allocate more time to learning about the structure of the Decision-Making Problem (DMP) and collect information that can be used to learn the requirements of the decision-makers to select or develop the most relevant MCDA method.
The suboptimal selection of MCDA methods implemented by the analysts so far implies that they have focused their choice of the MCDA methods on a subset of the relevant features to describe the DMP. The MCDA-MSS integrates, in its questions and answers, the 219 features that the authors of the MCDA-MSS (i.e. the MCIF fellow and his team) deem relevant to describe each DMP. Consequently, decision analysts now have software that can support them in their work to select an MCDA method (or a subset) that fits a detailed description of each DMP.
