This article will help you understand about saaty scale and its real-life applications
There are linear scales and then there are non-linear scales. Let’s try to understand both of them.
Linear scale:
This is nothing but a scale that we generally see in graphs, maps, … and so on. Basically, it is divided into linear intervals.
The above is a linear scale with an interval gap of 1.
This is also a linear scale with an interval gap of 25
Logarithmic Scale:
As the name suggests, this is a log scale, which is a type of non-linear scale.
The above picture gives you a clear explanation of comparision between a linear scale & log scale, ex: 100 on linear scale = 2 on a log scale [explanation: log(100 base 10) = log(10^2 base 10) = 2*log(10 base 10) = 2
Saaty Scale:
Now that we have a decent understanding of what a scale looks like, let’s try to understand saaty scale. It is developed by Thomas Saaty, a professor at the University of Pitsburg, also a researcher & inventor who developed a framework called AHP (Analytic Hierarchy Process) which is used to make decisions for complex problems.
Saaty Scale looks something like this
As we see, “1” is more like a neutral point and then you see numbers and their inverse on both sides, we’ll come back to the explanation of these in a minute.
Sometimes you may see a shorter version of this, which is also the same after ignoring a few levels:
Below is an explanation of these numbers
Example:
Let’s say you’re applying for a new job.
And you have chosen 3 parameters – Salary, Domain, Location
- Salary – Just like everyone, Salary is Extremely important to you as well.
- Domain – At this point, you’re just looking for a job, and you’re okay with any domain.
- Location – Bangalore is your favorite location and you’re strongly comfortable with it.
So, if we have to assign importance values for these 3 parameters according to Saaty Scale, here’s how it looks
- Salary – Saaty Scale (9 – Extreme importance)
- Domain – Saaty Scale (1 – Okayish importance)
- Location – Saaty Scale (5 – Strong importance)
You may not be 100% clear with the above example, but we’ll deep dive into it in this article on Pairwise Matrix.