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It is clear from these figures that in fine weather I prefer not to have to carry an umbrella, but only slightly, and that I have a fairly strong preference for fine weather over rainy weather. I could equally well have used other numbers to express these preferences. It is usual to use positive numbers for good outcomes and negative numbers for bad outcomes, but this is not necessary.

So should I take my umbrella? Well, it depends on how likely it is that it will rain today. Since the expected utility of taking my umbrella is greater, I should take my umbrella. Again, notice that the absolute value of these numbers is unimportant; only their relative value is significant.

Strictly speaking, the conclusion that I should take my umbrella only follows under the assumption that I want to maximize my expected utility. Some people take this as a descriptive principle about humanity; it is a true description of human beings that they want to maximize their expected utility. Others take it as a normative principle about rationality; if you are rational, you should attempt to maximize your expected utility.

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Still others subscribe to neither of these principles. Still, whether or not either of these principles hold, it is uncontroversial that there are circumstances in which people want to maximize their expected utility--for example, when I am deciding whether or not to take my umbrella. I may not explicitly go through the above calculation, but something like it goes through my mind.

If the chance of rain is high enough, I take my umbrella because I don't want to get wet, and otherwise I leave it at home because I can't be bothered carrying it.

Nobody needs an expected utility calculation to tell them whether or not to take an umbrella, but they can be help in situations where more is at stake. For example, suppose you are considering whether to have your child vaccinated against whooping cough. The whooping cough vaccination protects children from a potentially fatal disease, and also has some rare but serious side-effects.

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Let us suppose that unvaccinated children have a 1 in 50, chance of dying of whooping cough, which is reduced to 1 in 1,, by vaccination. Also, suppose that the vaccination carries a 1 in , chance of causing permanent brain damage. If you were to carry out this calculation seriously, you would first need to find some reliable estimates for these probabilities; my figures are not reliable!

To carry out the calculation, you need to assign utilities to the possible outcomes. Suppose you assign a utility of to your child's death, -8 to permanent brain damage, and 0 to normal health.

Expected utility theory

Remember that the absolute size of these numbers is arbitrary; only their relative size matters. That's how you would satisfy:. As for his slides and his "simplified Boltzmann equation". He's throwing in another variable and step that isn't really described. Apparently it has something to do with stress.

But you're not going to convert that chart from the left into the chart on the right because you don't have all the information needed. Because it's all kinda bullshit. It's the Sims dude, how deep did you think it was?

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§2. Examples and fallacies

Viewed times. You should describe what he describes. Nobody wants to go follow your link. Subsequently, we investigated the role of loss function asymmetries in Chapter 4.

The Role of Subjective Probability and Utility in Decision-making - Semantic Scholar

Across 4 studies we provide evidence for the notion that powerful, but not powerless individuals assign higher probabilities to negative events. Manipulating the controllability of the event, we provide evidence for loss function asymmetries as a mechanism underlying the impact of social power on the relationship of negative utility and probability estimates.


Having demonstrated that powerful individuals can be more sensitive to negative information, Chapter 5 shows across 3 experiments that the powerful act more on affordances of negative affective states. In sum, this thesis provides evidence for loss function asymmetries underlying the interdependence of utility and probability, and demonstrates that power can lead to heightened sensitivity for negative affective information.