Multi-criteria problems are common occurrences. For example, a farmer’s decision over which crops to plant may depend on factors such as expected revenues, risk of crop failure, time requirements or personal preferences, which need to be weighed against each other in order to find an optimal solution. However, direct comparison is often impossible because of the different dimension in which criteria are assessed (e.g., how to weigh a higher average profit against a higher risk of crop failure)?
Usually, such decisions are made intuitively based on previous experience. However, certain settings such as government, science or business optimisation require a more structured approach, either in the interest of transparency and accountability, or because problems are too complex to solve intuitively.
MCA is a term used for procedures that evaluate data from multiple categories in order to arrive at an integral score. It provides a tool for dealing with the inevitable trade-offs within complex decision-making situations that may also feature high uncertainty, different forms of information, and multiple stakeholder interests and perspectives. For this reason, MCA has gained momentum as a methodology for the evaluation of sustainability (Adams & Ghaly, 2007).