Well, usually we do have some sort of uncertainty in real-world problems.

And even if we had a decision with virtually no uncertainty, precisely finding the optimum might require computation.

Hope this helps.

Ola Ola,

Sound decision making is dictated by logical deductions. This means that a decision support system should better be a binary decision tree. Just like a binary plant determination table is a good example of a binary decision tree developed to decide with which plant species one is dealing.

Actually when an outcome is potential, it is an outcome which is by definition attributed a level of uncertainty. I have put this forward in an earlier thread, when you cannot boil down a series of questions in a decision tree to a yes or no answer (binary decision), then the question or problem is ill defined and hence only potentially apt for an absolutely certain decision.

Typically, when you cannot make a decision tree for example to determine plant species, you will never be able to identify new species in the plant world. The same is true for any other problem area in science. One is never absolutely certain that all solutions for a problem area are identified. Hence solutions are always doted with a level of uncertainty which you have to reduce as much as possible by a rigorous evaluation of the problem area by developing a binary decision tree, which requires thorough study and knowledge acquisition of the problem area. In many ways sound decision making is the art of reducing the uncertainty of known solutions in a specific problem area as much as possible. As if it were a statistical law, the more solutions found in a problem area the lower the uncertainty of each of these solutions.

No?

The more plant species known, the lower the uncertainty on erroneous determinations. Why else do botanists put so much effort in finding and describing new plant species and studying the relationships between the determined 'solutions', nature has developed over thousands of millenia.

Cheers,

Frank

Even if the potential outcomes are deterministic and all known in advance, decision-making is still a nontrivial problem if multiple criteria are involved, and if you there is no common scale on which to evaluate each individual criterion ("Multi-criteria decision making").

For example, if you want to buy a new car, there might be a number of different criteria that can only be met up to a certain point, but not all at the same time, for instance:

• The car should be fuel-efficient
• It should be able to carry a large number of persons (or a high amount of load)
• It should be fast
• It should require little parking space
• It should be cheap
• It should be robust and durable
• etc. ...

When you compare a given set of potential choices according to these criteria, you find out that a Ferrari is probably faster than a Ford, but more expensive; a pickup can carry more load, but consumes more petrol; ...

Thus, in order to make an optimal choice you need to find a method to determine the relative value of each property. This is quite tricky in general. I hope I could give you a brief illustration.

If you want to learn more, see, for example, "Analytic Hierarchy Process (AHP)".

Kind regards