1. MAUT is multi-attribute utility theory. It is a normative theory of decision making, meaning that it is meant to make the perfect decision under perfect circumstances. The first step in MAUT is to weight the dimensions, or the attributes of a particular choice. The next step is to rate the utility of each major on each dimension. Then, one should combine all ratings of all dimensions across alternatives (or choices). This involves multiplying the weight times the rating. Then, one should choose the alternative with the highest utility, or the highest overall rating. Utility can also be defined as taking all dimensions into account simultaneously. When using this theory, you have to be willing to make tradeoffs between dimensions – one dimension will often compensate for the other. MAUT cannot be used when trying to protect one dimension. Even though a choice may be highest in that dimension, it still may fall in overall utility to other choices.

2. One error (from the perspective of normative theory) that people make when making decisions is that they ignore the weights of the dimensions. In a study by Golotti, students selecting a major were asked to list the alternatives, dimensions, weights of the dimensions, and ratings of the alternatives on dimension. This (WS1) was then taken away from them, and they were given WS2 in which they were to rate their overall feeling of how good the given alternatives felt. Then subjects were given back WS1, and one group calculated ratings with the weights (weights and ratings summed) while another group calculated without the weights. WS1s were then compared to WS2. If people are using the weights in their head in WS2, then the calculation with the weights of WS1 will be more similar to WS2 than the calculations without the weight in WS1. However, there was no specific difference. This suggests that people are not using weights in the overall ratings (WS2) and ignore weights when they have to make decisions in their heads. The error is that normative models say that if one dimension is more important, it should have a bigger impact on the decision. In this case, it didn’t.
Another error is that people do not consider all dimensions simultaneously. One choice may have the highest utility, but it may have been cut from your list of options because you only considered one dimension at a time and that choice didn’t rate high enough on that particular dimension. You might end up missing – from a compensatory point of view – your perfect choice.