Assume that graduation is here and you've been offered a job with the choice of two cities. The job in both cities would be the same with the same salary (given cost of living adjustments). I’m adding this to take salary out as a deciding factor - so do not choose it as one of your factors below. A multi-attribute model is a tool to help someone assess what is most important in making a tough decision. For some decisions the task itself can be overwhelming. If you have ever purchased a house you are probably familiar with this difficulty. It can be difficult to compare dissimilar things given a large amount of decision criteria. To create and use a multi-attribute model, you first identify the important variables that help you make the decision, and then you identify how important each variable is in making the decision. You can rank them from most important to least important, but it is probably better to assign a weight value to each.

You need to create a multi-attribute model with 5 factors that would influence your decision about which of the two previously mentioned cities you would move to. To do this, first list 5 variables that would be important to you in making this decision. Next assign some value to those 5 variables. Again you can do this by ranking them, but the better way is to assign each a weight value from 1 to 10. Now that you have created the model, let’s see how well it works to choose for you.

If I told you the two cities were Atlanta, Ga and Tokyo, Japan, which would you choose - don't look at you model - just choose quickly?

Next go back to your model and examine each of these 2 cities using your model. Which city would your model choose for you?

Did you choose something different than your model chose for you? If yes, why do you think that was the case?

If no, do you then think your model was accurate in helping you make the decision?

Is there something else you would have included in you model if you knew what the potential city choices were before you created the model?