Decision Making Under Uncertainty
Making the Best Choice With the Information Available
According to statistician George Chacko, decision making is the "commitment of resources today for results tomorrow."
As such, decisions are usually made in a situation of some uncertainty, because we can never be completely sure what tomorrow will bring.
For example, imagine you were trying to decide between two candidates for a new sales job. One has considerable experience of selling in the field in which you operate, but has only an average performance history. The other has never worked with your type of product, but she's got a superb track record in another type of selling. You're effectively "comparing apples with oranges."
How do you pick the one who will generate the best future sales?
Alternatively, imagine that you're deciding whether you'll invest in a new project. Given an uncertain future (and therefore uncertain future sales) how will you decide if the additional sales you'll generate will justify the additional costs?
This is where you need to manage the level of uncertainty you're working with, so that you can make a decision based on rational, disciplined thought.
In both cases, the solution is to quantify the problem, although each involves a different approach. In the first, you need to turn qualities like "experience" and "sales ability" into numbers, so that you can compare them. In the second, you need to understand the ways that things may change in the future, and factor these into your decision.
We'll start by looking at how you can quantify your decision-making. We'll then move on to show you how you can factor different possible futures into your decisions.
Quantifying Non-Numerical Features
When the uncertainty you're working with arises from having to choose between unlike options, you'll need to work out how to quantify the elements of each option, so that you can make a direct, numerical comparison.
There are many tools that you can use to do this, ranging from simple plus/minus lists, through to sophisticated grid-based analyses.
One of the most common approaches that people use when making a serious decision is simply to look at the pros and cons of two alternatives. A good way of structuring this is to use the Quantitative Pros and Cons technique, which involves assigning numerical weights to each pro and con. Read our article for more details on how to do this.
If you need to compare more than one different option, and, in particular, if you need to rank options in order, then paired comparison analysis is a useful tool. This approach works by comparing each option with each other option, each time choosing the best option, and rating how much better it is, and then consolidating results to give a balanced answer. This type of analysis relies on a certain amount of intuition, so it's most useful where decisions are highly subjective, or where it's really difficult to identify or weigh the importance of the decision criteria.
Stepping up from the simple paired comparison approach, you can use a more sophisticated grid to weigh your alternatives. Also known as Grid Analysis, the Decision Matrix Analysis approach asks you to score your choices according to a set of weighted decision factors. It's particularly useful when you need to compare several different options using many different decision criteria.
One further option is to use the analytical hierarchy process. This is most useful when you have a lot of competing factors as well as different priorities and perspectives to consider. What's more, making decisions on your own can be difficult – but when you add the interests and viewpoints of other people, it's easy to spend so much time discussing the problem and negotiating a solution that you never make a decision.
The analytical hierarchy process (AHP) was developed to try to quantify the different needs and values of the various stakeholders and alternatives, helping you compare them in a rational manner. You take the intuitive part of a paired comparison, and then use that to assign weights to each decision factor. From there, it's easier to quantify the factors, and you can see the best alternative.
AHP is very complex and time-consuming. For a detailed explanation and a step-by-step example of analytical hierarchy process, see our comprehensive article. And remember, before using this approach, to make sure that a simpler grid method doesn't give you the answer!
Looking at Different Futures
Our second group of techniques involves looking at the different ways in which the future may turn out, and making decisions based on these.
The simplest and most purely numerical way of doing this involves creating a decision tree. Tree diagrams provide a useful way of organizing your options visually, and of thinking about the consequences of each of these. By constructing a decision tree, you can calculate the risks and potential rewards of the alternatives in a way that makes it easy to interpret the results.
See our article on decision tree analysis to work through a detailed example of how to construct one.
Although decision trees allow you to factor in the likelihood of various futures actually occurring, they can't really cope with the unpredictable variations or even randomness that occurs in everyday life. For example, your profits could be affected by more than just sales levels. What if raw materials prices went up, or demand was affected by unseasonal weather?
A good way of considering factors like these is to use two techniques – scenario analysis and Monte Carlo analysis – together.
With scenario analysis, you think about all of the ways that things may change in the future, and from this list, identify the changes that are most likely to occur, and which could have the greatest impact on your decision. For each of these, you develop scenarios that explore these different futures. Using our earlier example, you might look at what the future looks like if raw materials become very expensive, what it looks like if they stay the same, or what happens if raw material prices drop significantly.
You then repeat the exercise for one or more alternative futures.
You can then use Monte Carlo analysis to model your decision across all of these scenarios. (The name "Monte Carlo Analysis" refers to the casinos at Monte Carlo in Monaco, where hundreds of chance events happen every day.)
The idea behind the technique is that you set up probability distributions – representing your scenarios – within the forecasting model you're using to make your decision, and then feed random numbers generated by these probability distributions into this model. After hundreds of sets of random numbers, the consolidated probability distribution that comes out at the other end shows the most likely consolidated outcome when everything has been taken into consideration.
This is far from straightforward to carry out, but it's considerably more sophisticated – and comprehensive – than a basic tree diagram. What's more, it gives you a very good understanding of your decision, and of the way that the future may turn out.
Unless a decision is entirely a matter of personal choice (would you prefer to have the team meeting in the conference room or in your office?), most decisions involve some level of uncertainty.
That doesn't mean that you have to resort to guesswork to make the decision.
One approach is to quantify the non-numerical aspects of the options between which you are choosing. Another is to consider the most likely alternative futures in which your chosen option may exist, and look at what the outcomes of your decision are likely to be in these futures.
We've listed many different tools that help you make decisions under these types of uncertainty. Practice using these to evaluate your options: while you'll never be able to eliminate all uncertainty in your decisions, they'll give you the skills and confidence you need to find your best alternative, given the information you have available.