Comment author:jimmy
10 July 2011 06:17:53PM
4 points
[-]

Well, in two pictures it sums up loss aversion, scope insensitivity, overestimation of high probabilities, underestimation of low probabilities, and the framing effect. There's no information on there that corresponds to non-testable predictions, and the framing effect is a very real thing- you can often pick it for people.

It doesn't seem to simplify anything either, since the curves have to be justified by experiment instead of some simple theory, but it is a conveniently compact way of quantitatively representing what we know. How would you make quantitative statements about how loss aversion works without something equivalent to prospect theory?

I agree that the left curve (subjective value of monetary loss/gain) shows loss aversion and maybe scope insensitivity (there's only so much pain/reinforcement our brain can physically represent, and most of that dynamic range is reserved for routine quantities, not extreme ones), at least for money.

I'm not sure how the right curve, which I presume is used to explain the (objectively wrong under expected utility maximization) decisions/preferences people actually take when given actual probabilities, shows over- or under- estimation of probabilities. If you asked them to estimate the probability, maybe they'd report accurately - I presumed that's what the x axis was. If I use another interpretation, the graph may show under-estimation of low probabilities, but ALSO shows under-estimation of high probabilities (not over-estimation). Could you explain your interpretation?

Otherwise, I agree. These curves take these shapes because they're fit to real data.

I'm curious if the curves derived for an objective value like money, are actually predictive for other types of values (which may be difficult to test, if the mapping from circumstance to value is as personally idiosyncratic as utility).

## Comments (46)

BestWell, in two pictures it sums up loss aversion, scope insensitivity, overestimation of high probabilities, underestimation of low probabilities, and the framing effect. There's no information on there that corresponds to non-testable predictions, and the framing effect is a very real thing- you can often pick it for people.

It doesn't seem to simplify anything either, since the curves have to be justified by experiment instead of some simple theory, but it is a conveniently compact way of quantitatively representing what we know. How would you make quantitative statements about how loss aversion works without something equivalent to prospect theory?

I agree that the left curve (subjective value of monetary loss/gain) shows loss aversion and maybe scope insensitivity (there's only so much pain/reinforcement our brain can physically represent, and most of that dynamic range is reserved for routine quantities, not extreme ones), at least for money.

I'm not sure how the right curve, which I presume is used to explain the (objectively wrong under expected utility maximization) decisions/preferences people actually take when given actual probabilities, shows over- or under- estimation of probabilities. If you asked them to estimate the probability, maybe they'd report accurately - I presumed that's what the x axis

was. If I use another interpretation, the graph may show under-estimation of low probabilities, but ALSO shows under-estimation of high probabilities (not over-estimation). Could you explain your interpretation?Otherwise, I agree. These curves take these shapes because they're fit to real data.

I'm curious if the curves derived for an objective value like money, are actually predictive for other types of values (which may be difficult to test, if the mapping from circumstance to value is as personally idiosyncratic as utility).