FQ 8 #1-8

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.

3. Framing effects demonstrate that the outcome of a decision can be influenced by two factors: the background context of the choice and the way in which a question is worded. Framing effects are inconsistent when evaluating utilities. The error, from the normative model, is that the utility should be the same if the facts are the same, but this is not the case with framing effects. Framing effects can be illustrated in two separate problems that have the same deep structure – the amount lost or gained is the same. However, the amount seems bigger when expressed as a loss than when expressed as a gain. For example (list own personal example here).

4. According to the representativeness heuristic, we judge that a sample is likely if it is similar to the population from which the sample was selected. People tend to base their decisions on representativeness rather than actual probability. People also tend to ignore the base rate, or how often the item occurs in the population. When given a description of a man that is likely to be engineer, we will say that (because of the representative heuristic), he is more likely to be an engineer than a lawyer even if the population consists of 30 engineers and 70 lawyers. We also commit the conjunction fallacy – we judge the probability of the conjunction of two events to be greater than the probability of either of its constituent events. The combination of the two constituents makes the description seem more representative and plausible. (Give an example?)

5. According to the availability heuristic, we estimate frequency and probability in terms of how easy it is to think of examples of something. Recency plays a role in this heuristic – memory for items generally declines with the passage of time. Thus, we recall more recent items more accurately, and recent items are therefore more available. For example, if I’m currently overloaded with work from this quarter’s classes, I’m more likely to report that classes in general at UW give way too much homework. Familiarity also plays a role – we estimate higher frequencies of things that are more familiar to us. If I know many divorced individuals, I’m likely to estimate higher nation divorce rates (book example).

6. According to the anchoring and adjustment heuristic, we begin with a first approximation – an anchor – and then we make adjustments to that number on the basis of additional information. People tend to rely too heavily on the anchor, and adjustments are often too small. The anchoring and adjustment heuristic is often based on the availability heuristic because highly available information is likely to serve as an anchor. For example, when we meet someone from a particular group, we are likely to rely on stereotypes in order to create an initial anchor. Then we consider the unique characteristics of that individual and make some adjustments. Sometimes we may not make sufficiently large adjustments away from that initial anchor. We use this heuristic when estimating confidence intervals, or ranges within which we expect a number to fall a certain percentage of the time. We tend to make our confidence intervals way too narrow because, as stated earlier, our adjustments away from the anchor are way too small.

7. One assumption is that a perfect decision is always required. This would be good for, say, cancer treatments. However, an adequate decision is often fine. Normative model says that alternative and dimensions are static. In reality, information is dynamic. Normative model assumes that we have plenty of time to deliberate, which actually, in real life, decisions are made quite quickly (like in the situation of the fire commander). Normative model also assumes that all information is available simultaneously and that alternatives are mutually exclusive. Information is really, however, often incomplete and alternatives are not necessarily mutually exclusive. Normative model also assumes that decisions are self-contained. In real life, decisions are often part of action-feedback loops. Situation assessment is figuring out what the alternatives and what the dimensions are. NM emphasizes choice point – selecting among alternatives. In real life, when people have expertise (such as the fire commander), they only really consider one alternative.

8. Overconfidence means that people’s confidence judgments are higher than they should be, based on their actual performance on a task. People are often overconfident in estimating their future performance and even their assessment about other people. People also commit the planning fallacy – they typically underestimate the amount of time or money needed to complete a project; they also estimate that the task will be relatively easy to complete. This had certainly happened to me – before delving into an English paper, I completely underestimated that time it would take to finish it and was pretty stressed out at the last minute. This is related to decision making because it helps me choose to not allot as much time as I really need. Overconfidence is typically based on the following: 1) people are often unaware that their knowledge is based on very tenuous and uncertain assumptions or unreliable information; 2) examples for confirming our hypotheses is readily available, whereas we resist searching for counterexamples, 3) people have difficulty recalling different hypotheses, 4) when able to recall different hypotheses, people don’t treat them seriously, and 5) a self-fulfilling prophecy operates.