Bringing order from confusion.
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.
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...
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