Stacy has just returned from vacation and thus she has been out of town. Since variety objective makes a difference only when Stacy visits a restaurant in the past week, we disregard this objective at this point. Then, since distance and price are equally important, their weight should be 0.50.

Next, with two remaining objectives, the decision tree looks as follows:

Now, we need to assign scores to each consequence. As far as the price is concerned $2.5 yields a score of 100 and $12 yields a score of 0. Then by linear interpolation, we could assign a score to each price level using the formula (12 X)/(12 2.5) * 100. Keep in mind, this formula is just a heuristic and is not necessarily relevant for all individuals/decision makers. But, lets assume this is what represents Stacys preferences.

For the distance attribute, we assign a score of 100 to 2 minutes and 0 to 10 minutes. Then a score for the distance attribute would be computed using a similar formula (10 Y)/(10 2) * 100.

Then the updated Sam's Pizza decision tree would like

Using the price level formula (12 X)/(12 2.5) * 100, we can calculate Lowest Price of Sam's Pizza as: (12 - 3)/(12-2.5) * 100 = 94.74. Similarly, Average Price of Sam's Pizza as (12 - 3.5)/(12-2.5) * 100 = 89.47.

In order to answer part (a) and part (b), complete the rest of the tree using price level formulas and distance formula. Then, calculate the EMVs to make your final decision.