- MCDA for a personal decision
by Elizabeth Atherton
This article gives an overview of multi-criteria decision analysis (mcda) and the advantages of using it to structure decision problems. It includes a description of the use of mcda for a personal decision problem. The analysis was carried out informally on the job offers available to S. The decision problem was then modelled using Analytica, Bayesian updating and sensitivity analysis. The results modelled the decision maker’s opinions exactly. This resulted in him being able to negotiate much better working conditions, due to an increased understanding of his situation and what was important to him.
The aim of decision analysis is to help to improve decision makers’ understanding of their problem and to guide them towards the ‘best’ decision. mcda helps people to evaluate complicated situations in terms of their values (what they care about) and uncertainty (what they know or do not know). mcda can often give insight into how alternatives differ from one another and help people to identify new and improved alternatives.
Decisions are often difficult to make because:
They are complicated -
there are too many things to think about at once or there are different opinions which need to be considered.
There is uncertainty -
the decision maker does not know what will happen in the future or does not really know how they feel about parts of the decision.
Multiple objectives -
the decision maker is trying to achieve several goals at once and this may involve conflicting goals which need to be traded off.
Multiple perspectives -
if there are several decision makers they may have different views and opinions about the situation. Small changes in opinion may change the decision made.
mcda involves several evaluation processes which help to solve the problems identified above. The processes often overlap and may need to be performed several times to obtain a model that captures the decision makers’ thoughts and beliefs clearly enough for a decision to be made. This is because as the procedure is undertaken the decision makers’ understanding of the problem is increased and their opinions and thoughts develop. The order in which each of the processes is conducted, and how they are conducted is determined by the decision and its structure. There is no definite sequence which can be applied to all decisions, and even if decisions are classified to be of a certain ‘type’ the analysis will still depend on the specific nature of the decision and those involved in the process.
The steps of mcda
mcda involves the following steps:
Identify the stakeholders
These are people who have resources to influence how the decision is made or are affected by the decision. This can clarify where people may have different perspectives and who needs to be involved in the decision process. It can help to identify things that the decision makers want to achieve or avoid.
The objectives are what the decision makers are trying to achieve or what they want to avoid. Specifying them helps the decision makers to determine what is important to them. It can identify where conflicts might arise and can help to clarify the complicated issues which are involved in the decision. It can also help the decision makers to create new alternatives to meet their aims.
Decision makers have to identify the alternatives available to them, and it also gives the chance to create new alternatives which they could implement. If there are lots of alternatives it may be difficult to analyse them without using a computer package, using a package can help to decrease the time needed to do the analysis.
This stage involves identifying what is going to be used to evaluate how well an alternative achieves an objective. It breaks down the objectives into achievable and measurable goals, it can highlight conflicts in objectives and may identify alternatives which are not feasible. Examples of an attribute tree and influence diagrams are given in figures 1 to 4. Analytica has a graphical user interface which allows decision makers to see their problem pictorially, this can help them to understand their problem better. Analytica also allows decision makers to have multiple level influence diagrams which stops the screen from being over-crowded if there are lots of attributes. For example, figures 2 to 4 are all displayed on separate screens within Analytica. It also gives decision makers the ability to focus on one aspect of the problem at once, and can help to group the attributes and identify any conflicts.
Scenarios are states of the world that might occur after the decision is made, identifying them can help to show where there is uncertainty about the future and about the outcomes of a decision. This can help decision makers to identify areas that they need to investigate and can help to quantify the uncertainty.
Collect data and/or consult experts
The data necessary to make the decision may already be available, or the previous processes may have identified areas that need investigating. This process involves determining how well each alternative performs on each of the attributes.
Determine decision makers’ preferences
This involves identifying the relative importance of the objectives and therefore the attributes to the decision makers. It helps to identify what really matters in the decision and how much it matters. It can help to eliminate alternatives and can clarify what the decision makers are trying to achieve.
Determining which decision alternative is the ‘best’ can be difficult if there are lots of alternatives available, involving many attributes and if there is uncertainty surrounding the decision. In these cases computer packages can be used as they have the ability to analyse numerous alternatives against lots of attributes quickly. The uncertainty can be included via probability distributions and alternative scenarios of the future can be built into the model.
This process helps to determine how ‘robust’ the decision is, and whether it changes when the inputs are changed. It can be used to perform what if analysis of the future and can determine what affects the decision. Sensitivity analysis can identify if the uncertainty in the model affects the decision and can show where it might be useful to gather more information. The decision can be analysed from different perspectives to see if the decision changes under different view points. Any input values which the decision makers are unsure of can be modified to determine the effect of the changes. Again, computer packages can help, they allow users to consider many different scenarios quickly and easily and allow them to change values quickly. Analytica allows decision makers to change several input values at once and so the combined effects of changes can be analysed.
It may at first glance seem that mcda is very time consuming, but the rewards generally far out weigh the extra time required. Decision makers comment that they have a clearer understanding of the issues and feel more confident with their decisions. It is also easier to justify the decision made as all the thought processes involved have been clearly mapped out. The decision model itself is more likely to include all the important aspects and because the decision makers have thought about what might happen in the future, they are less likely to be surprised by an unexpected event. More detailed descriptions of how to perform decision analyses can be found in Edwards and Von Winterfeldt (1986) and Watson (1987).
To illustrate that mcda can help, even for personal decisions the following sections outline a decision problem that was analysed using mcda.
S’s decision problem
S is an actuary who had been offered two jobs. He already had a job and needed to decide which, if either, of the jobs he would like to take. I offered to help S to think about the issues involved in his decision and to help him to think about how important each of them were to him. At first S did not seem to realise that staying with his current firm was in itself a choice. After I had explained to him that this was an active decision and that we needed to analyse his current job in the same way as his job offers, we had three alternatives to consider. The act of defining his current job as a choice may have also decreased the status quo bias, as S had to evaluate it as a new possibility.
As this was a personal decision, S and his family were the only stakeholders, and he thought that his opinions reflected his wife’s and would take into consideration the needs of his one year old son. The analysis was carried out very informally at S’s house, which helped S to feel relaxed.
The whole process consisted of two meetings, approximately one week apart. The first meeting was the longest. At this meeting I helped S to identify the attributes, evaluate the alternatives with respect to each of the attributes and identify the relative weights of the attributes. The second meeting was used to update the model in light of new information which S had obtained and to perform sensitivity analysis on some of the attribute weights and values about which S was unsure.
At the beginning of the first meeting I explained carefully the aim of the meeting. I described the concept of multi-criteria decision analysis (MCDA) and how we would elicit S’s values and opinions. S found it easy to understand the ideas behind MCDA as he had a mathematical background. We discussed how S could include uncertainty in the model, and he mentioned that this might be useful. I outlined the stages of MCDA that we would go through, namely identifying attributes, evaluating the alternatives with respect to the attributes, evaluating scenarios of the future, weighting the attributes, optimising the decision, iterating and performing sensitivity analysis.
The whole process was carried out very informally and at a pace that S was happy with. At several points during the analysis I reviewed what we had achieved, to ensure that I had understood S clearly and to help him to clarify things in his mind.