A lot of rationalist thinking about ethics and economy assumes we have very well defined utility functions - knowing exactly our preferences between states and events, not only being able to compare them (I prefer X to Y), but assigning precise numbers to every combinations of them (p% chance of X equals q% chance of Y). Because everyone wants more money, you should theoretically even be able to assign exact numerical values to positive outcomes in your life.
I did a small experiment of making a list of things I wanted, and giving them point value. I must say this experiment ended up in a failure - thinking "If I had X, would I take Y instead", and "If I had Y, would I take X instead" very often resulted in a pair of "No"s. Even thinking about multiple Xs/Ys for one Y/X usually led me to deciding they're really incomparable. Outcomes related to similar subject were relatively comparable, those in different areas in life were usually not.
I finally decided on some vague numbers and evaluated the results two months later. My success on some fields was really big, on other fields not at all, and the only thing that was clear was that numbers I assigned were completely wrong.
This leads me to two possible conclusions:
- I don't know how to draw utility functions, but they are a good model of my preferences, and I could learn how to do it.
- Utility functions are really bad match for human preferences, and one of the major premises we accept is wrong.
Anybody else tried assigning numeric values to different outcomes outside very narrow subject matter? Have you succeeded and want to share some pointers? Or failed and want to share some thought on that?
I understand that details of many utility functions will be highly personal, but if you can share your successful ones, that would be great.
What counts as a "successful" utility function?
In general terms there are two, conflicting, ways to come up with utility functions, and these seem to imply different metrics of success.
The first assumes that "utility" corresponds to something real in the world, such as some sort of emotional or cognitive state. On this view, the goal, when specifying your utility function, is to get numbers that reflect this reality as closely as possible. You say "I think x will give me 2 emotilons", and "I think y will give me 3 emotilons"; you test this by giving yourself x, and y; and success is if the results seem to match up.
The second assumes that we already have a set of preferences, and "utility" is just a number we use to represent these, such that xPy <=> u(x)>u(y), where xPy means "x is preferred to y". (More generally, when x and y may be gambles, we want: xPy <=> E[u(x)]>E[u(y)]).
It's less clear what the point of specifying a utility function is supposed to be in the second case. Once you have preferences, specifying the utility function has no additional information content: it's just a way of representing them with a real number. I guess "success" in this case simply consists in coming up with a utility function at all: if your preferences are inconsistent (e.g. incomplete, intransitive, ...) then you won't be able to do it, so being able to do it is a good sign.
Much of the discussion about utility functions on this site seems to me to conflate these two distinct senses of "utility", with the result that it's often difficult to tell what people really mean.
When I teach decision analysis, I don't use the word "utility" for exactly this reason. I separate the "value model" from the "u-curve."
The value model is what translates all the possible outcomes of the world into a number representing value. For example, a business decision analysis might have inputs like volume, price, margin, development costs, etc., and the value model would translate all of those into NPV.
You only use the u-curve when uncertainty is involved. For example, distributions on the inputs lead to a distribu... (read more)