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There are essentially four major decision-making models: ad-hoc decision-making, multi-attribute utility theory (MAUT), AHP, and Bayesian team support (the model that underlies the Accord tool suite). A brief introduction to these and how they compare follows.
Ad-hoc decision making is what we naturally do when faced with an issue, especially when there is not time or other incentive to take a more structured approach. Exactly what people do "ad-hoc" depends on the problem (task variables, context variables); the person (cognitive ability, prior knowledge); social context (accountability, group membership) and time available. People often try to minimize their effort by using a strategy to determine the most important criteria and compare their first idea to it. If this idea meets the important criteria then they choose it. If not then they will develop a second alternative and if two or more alternatives look about the same, they will begin to look at multiple criteria for evaluation.
Multi-attribute problems are those where there are multiple criteria (i.e. goals or attributes). There are both detailed mathematical and informal methods for managing multi-attribute problems. The informal methods are generally based on building a table with the alternatives heading the columns and the criteria heading the rows. For each criterion, a weighting that describes its relative importance is also captured. Then a score is entered into each cell that reflects how well the alternative meets the criterion. This score is multiplied times the weighting and summed for the alternative. Popular names for this method are Pugh's method and decision matrix. A template for doing these operations is available.
Analytical Hierarchy Process (AHP) was developed in the 1970s. It is an extension of MAUT and relies on pair-wise comparisons.
Bayesian team support was developed in the late 1990s by Robust Decisions to support decision-making with uncertain, team information. It too is a form of MAUT, but has unique mathematical model that allows the management of uncertain information and the fusion of inconsistent evaluations.
Making the decision about what decision-making support method to use
To make a decision, you must first make a decision about which decision-support to use. Below is a comparison the top four methods: Ad-hoc, Pugh's method, AHP and Accord. The criteria for comparison are:
- Low time required to frame a problem: This is a measure of the time needed to set up a problem—to merely enter the alternatives, criteria and other information. This measure does not count the time needed to actually formulate the problem which is the same for all support systems.
- Low time required to evolve the problem: Decision-making is not static. It is iterative with new information being developed, as the situation is refined. Thus, a useful method must allow for changes to the alternatives and criteria.
- Support for inconsistent, incomplete and uncertain information: Most decisions are based on information that is inconsistent (maybe even conflicting), incomplete and uncertain. A decision-making system that does not have this built into its foundation is glossing over the hardest part of making decisions.
- Support a shared team vision: One of the key goals for any decision support system is to gain buy-in and accountability from the team members and other stakeholders. This requires an environment that encourages win-win communication.
- Ability to manage complexity: Decision problems get complex fast. It is essential to not add to the complexity but make it easy to visualize and communicate.
- Support viewpoint variations: It is essential to honor what people think is important (their viewpoint) and not let it dominate the decision deliberation. A decision-support method that integrates viewpoint with alternative evaluation makes the situation more complex.
- Guide what-to-do-next: Arthur C. Clark once said, "The only hard decision in life is what to do next." In working to reach a decision, it is important that the method direct the team's course of action. It must identify what work to do to differentiate the alternatives from each other with the least amount of effort.
- Leave a logic trail: It is often important to vet earlier decisions, see why they were made and possibly reuse them for training and new situations.
- Reasonable cost:
In brief table form, the four alternatives measure up to these criteria as:
| |
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Ad-hoc |
Pugh's |
AHP |
Bayesian Team support: Accord |
| 1 |
Low time required to frame a problem |
No support |
List alternatives and criteria |
About twice as long as Pugh's or Accord1 |
List alternatives and criteria |
| 2 |
Low time required to evolve |
No support |
None |
About twice the effort as in Accord1 |
Easy |
| 3 |
Support for inconsistent, incomplete and uncertain information |
None |
None |
None |
Basis of methodology so very robust here |
| 4 |
Support a shared vision |
No |
Not well |
Too complex to build a shared vision |
Yes, clear GUI and server based |
| 5 |
Ability to manage complexity |
No |
Somewhat |
Adds complexity |
Yes, through GUI |
| 6 |
Support viewpoint variations |
No |
Not well |
Not well |
Designed to support multiple viewpoints |
| 7 |
Suggest what-to-do-next |
No |
No |
No |
Yes, guides to the most cost effect next steps |
| 8 |
Leave a logic trail |
No |
Somewhat |
Yes |
Yes, data base of deliberation |
| 9 |
Low cost |
Free |
Free |
Expensive |
Moderately priced |
1 See data in Comparison of Time required to Perform AHP and BTS Decision Analysis (PDF).
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