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Sustainability Assessment based on Decision Aiding

Periodic Reporting for period 1 - SAbDA (Sustainability Assessment based on Decision Aiding)

Reporting period: 2018-09-26 to 2020-09-25

• What is the problem/issue being addressed?

Our society is mostly based on the exploitation of finite resources. 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. These impacts and costs can be related to the environment (e.g. energy consumption, waste production), economy (e.g. raw materials costs, liabilities), and society (e.g. workers’ safety, development of local communities). Due to the multitude of impacts and costs, there is a clear need for methods that can convey in a clear and transparent form the overall performance of competing alternatives, such as 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 by aggregating the information to provide a decision recommendation, which can be a ranking, sorting, or choice.
A large number of MCDA methods are available, and there is currently no Decision Support System (DSS) capable of leading a decision analyst in the complex process of selecting the relevant method(s) for a specific decision-making problem. This is thus the main 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. A large number of MCDA methods are available, and there is currently no 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 is thus the central challenge tackled by this project.

• What are the overall objectives?

This project aims to formalize and contextualize the current MCDA methods leading to the development of a comprehensive DSS that selects the most relevant MCDA method(s) for solving decision-making problems. The DSS will be tested in the areas of Alternatives Assessment (AA) (e.g. materials, products, and technologies assessment) to assess its performance, intelligibility, and updatability.
• First part
A preliminary version of the decision support framework has been proposed, based on the available approaches that have been advanced to select an MCDA method or to conduct an MCDA process. The framework has been published as a taxonomy in OMEGA journal (https://doi.org/10.1016/j.omega.2020.102261).

• Second part
The decision support framework has been implemented in a DSS (beta version) to recommend the MCDA method(s) suitable for a given decision problem.
• Contribution to the state of the art

First part
1. Propose a unified and comprehensive high-level representation of the MCDA process characteristics;
2. Show how decision making can be split into manageable and justifiable steps, which can reduce the risk of overwhelming the analyst, as well as the DMs/stakeholders during the MCDA process;
3. Propose a questioning strategy to demonstrate how to apply the taxonomy to map MCDA methods and select the most relevant one(s) using real case studies.

Second part
The DSS includes a vast database of more than 100 MCDA methods. This database has two unique advantages when compared to the ones in the available DSSs that try to accomplish a similar task:
1. It covers a much larger set of MCDA methods;
2. It covers a much broader set of features to describe the MCDA process.

The main contributions of the DSS include the capabilities of:
1. Covering from very simple to very complex decision-making problems;
2. Offering recommendations for decision-making problems that do not perfectly match the DSS infrastructure;
3. Helping analysts prioritizing efforts for reducing knowledge gaps in the description of the decision-making problems;
4. Unveiling methodological mistakes in the selection of the methods.

• Expected results until the end of the project

The DSS will be finalized and then tested in (i) a broad set of literature case studies, and (ii) in projects that were and are part of the research portfolio of the two partner organizations of the Fellowship, namely the U.S. Environmental Protection Agency and the Paul Scherrer Institute.

• Potential impacts

The DSS has the capacity to redefine the procedure that will be used by analysts when conducting their search process for an MCDA method or a set of these methods. Given the increasing trend of use of MCDA methods in many disciplines and across disciplines, the DSS can thus shape how decision-making will be conducted for several audiences, including researchers, businesses, and policy makers.