Analytical Hierarchy Process (AHP) as a MCDA Method

First developed by Saaty (1980), AHP is a multi-criteria decision-aid methodology based on a carefully structured mathematical set of matrices and their associated eigenvectors to compare criteria or alternatives in a pairwise mode against some predetermined objective (Saaty, 1980; Saaty, 2008b). AHP is based on the principles of decomposition, comparative judgement and synthesis of priorities. Decomposition requires that a decision problem be decomposed into a hierarchy that captures the essential elements of the problem. Comparative judgement calls for the assessment of pairwise comparisons of the elements within each level/cluster (local maxima) of the hierarchical structure in relation to a parent in the higher level cluster (Malczewski, 1999). In the synthesis phase, each derived ratio scale local maximum is combined with other local maxima of each level, to construct a composite set of priorities for the elements at the lowest level of the hierarchy. This is followed by the aggregation of the scores of the previous levels to produce the composite score at the highest level of each hierarchy, known as the global maxima.

The ability of AHP to decompose a complex problem into a hierarchical structure of homogeneous clusters, coupled with its ability to capture, measure and synthesise individual preferences of qualitative and quantitative attributes into ratio scale weights, make the method appropriate in establishing climate change response priorities and subsequently allocating resources to chosen priorities (Hwang & Syamsuddin, 2010).

The flexibility and simplicity of the method has been proven in practice and validated by physical and decision experiments (Vaidya & Kumar, 2006; Forman & Gass, 2001; Saaty, 2008b), making it useful in the private and public sectors at strategic and operational levels in broad areas of choice decisions. Examples include hiring decisions or student admissions, prioritisation and evaluation (Hwang & Syamsuddin, 2010; Syamsuddin & Hwang, 2009; Chiu,

et al., 2004), resource allocation e.g. investment decisions and plant location decisions, benchmarking (Vaidya & Kumar, 2006), public policy (Satty, 2008a), health care and strategic planning (Meziani & Rezvani 1990; Ossadnik, 1996; Kurttila et al.,2000), quality control programmes and even political decisions, where the Institute of Strategic Studies used AHP to vote on the removal of Apartheid in South Africa and the release of Nelson Mandela from prison (Satty, 2008a).

It is against this backdrop of a diversity of applications that AHP is proposed as the suitable multi-criteria approach to quantify and rank the possible set of initiatives and activities that a business could employ to mitigate and adapt to climate change risks, and capitalise on available opportunities. The next section describes in detail the merits for using AHP in this study.

5.5.1 Structuring complexity

Decision theory postulates that the human mind functions better in an ordered experience, where complex systems are hierarchically classified and subdivided into homogeneous clusters of factors (Simon, 1996; Saaty, 1980). AHP simplifies decision making by breaking down a complex problem into its constituent parts, and then aggregating the solutions to all these sub- problems into one solution. The decision problem is structured as a hierarchy of constituent parts, where the main objective of the decision problem is the top constituent of the hierarchy tree and the alternatives are at the very bottom of the hierarchy tree (Satty, 2008a), with sub- objectives or criteria in between. The underlying philosophy is based on the ability of human beings to make better judgments on smaller, simpler, common-sense kind of problems (Satty, 2008b; Saaty, 1980). The tree structure used to formulate an AHP problem provides a clear, organised and logical view of the climate change response problem, making it easy for decision makers to visualise the problem and analyse it systematically at each level from the more general higher level constituents to the more specific lower level constituents (Satty, 2008a). The AHP tree structure is constructed by decomposing a decision problem into its constituent elements (Vaidya & Kumar, 2006) in a top-down approach.

Hayek (1956) stated that models of a complex system should consider two closely interrelated dimensions - the number of variables within the system and their interconnectedness. The implication is that the importance of any element of the AHP structure can only be understood

in terms of its relationships to other elements of the structure, and where the said structure can only emerge if the said elements have particular sorts of relationships. The climate change challenge is a network of such connectedness, as described in earlier chapters.

5.5.2 Interaction of Decision Makers with Model

In their study of information sharing within the USA intelligence network‘s on terrorism, Cook et al. (2007) came to the conclusion that decision making is not the rational processes that some might suggest, because they are compromised by factors outside the events that people seek to control and manipulate. This is equally true for decisions in commercial operations such as nuclear power plants, chemical process industries, oil and gas production, transport operations, air traffic control and in almost every other sphere of complex decision making, where economic factors impinge on the decision making process and risks are high (Teece et al., 2002).

In South Africa, stakeholders such as communities have a vested interest largely driven by broad based black economic empowerment (BBBEE), an initiative seeking to redress the economic imbalances brought on by Apartheid. Other stakeholders such as environmental activists, customers, suppliers, employees, shareholders, regulatory and statutory bodies also have their value systems that influence the types of decisions taken. Therefore particular attention has to be paid to reflect their interests and viewpoints in the climate change response decisions. Consultation and engagement with these stakeholders through a participatory process ensures that their values and interests are incorporated into the response decisions, thereby increasing the legitimacy of an organisation (Reyers et al., 2011).