assumption that utility maximization is a good approximation of human behaviour.
It is possible that modelling humans as utility functions works with a large aggregate of humans, but is not accurate on the individual level. The deviations from utility maximization probably cancel out in a large crowd.
assumption that utility maximization is a good approximation of human behaviour
they mean “well we looked at the behaviour of 1000 people, and lo and behold, it fits this utility function U!”
each person individually could have preferences nothing like U. The average of {1,1,1,1,1,1,1,1,1,1000} is about 100, but no individual number is anywhere near 100.
Deviations from utilility maximization (i.e. irrationality) will likely cancel out en masse.
Furthermore, economists will generally model some small part of human behaviour—e.g. purchasing tradeoffs in a particular domain—but I have never seen an economist model the entire human preference set. This is probably because it is too complicated for a team of expert economists to write down—never mind the poor individual concerned.
Good point, especially when it comes to markets. You can have a lot of people acting in predictably irrational ways, and a few people who see an inefficiency and make large sums of money off of it, and the net result is a quite rational market.
Average of large number of functions that look nothing like U has little reason to look much like U. The fact that something like U turns out repetitively needs an explanation.
It’s true that usually only a small portion of human behaviour is usually modeled at time, but utility maximization is composable, so you can take every single domain where utility maximization works, and compose it into one big utility maximization model—mathematically it should work (with some standard assumptions about types of error we have in small domain models, assumptions which might be false).
utility maximization is composable, so you can take every single domain where utility maximization works, and compose it into one big utility maximization model—mathematically it should work (with some standard assumptions about types of error we have in small domain models, assumptions which might be false).
Sure! I don’t doubt this at all. I’m not saying that you cannot in principle build some humongous utility function that is a justifiable fit to what I, a particular human, want. BUT the point is that it isn’t feasible in practise—hence my statement in the original post:
I will claim that trying to run your life based upon expected utility maximization is not a good idea
What I was trying to do was more trying to figure out rough approximation of my utility function descriptively, to see if any of my actions are extremely irrational—like wasting too much time/money on something I care about very little, or not spending some time/money on something I care about a lot.
OK, but then the question is how do you approximate a mathematical function from a set X to R, especially when your biggest problem is not being able to enumerate the elements of X? If you miss out most of the elements of X, then no possible assignment of numbers to those that you do include will constitute a good approximation to the function.
Approximation is likely to be a list of “I value event X relative to default state at Y utilons”, following economic tradition of focusing on the marginal. Skipping events from this list doesn’t affect comparisons between events on the list.
Next I look at utilons to costs ratios, and do more of things which result in events with high ratios, and less of things which result in events with low ratios.
By the way, as the function is marginal, value of money will be approximately linear, extra $100 is worth pretty much 100 times more than extra $1, it only breaks down on very large $s that significantly affect your net worth.
It is possible that modelling humans as utility functions works with a large aggregate of humans, but is not accurate on the individual level. The deviations from utility maximization probably cancel out in a large crowd.
Aggregation wouldn’t really work unless utility function was a pretty decent approximation, and its errors were reasonably random.
I think that when economists say that
they mean “well we looked at the behaviour of 1000 people, and lo and behold, it fits this utility function U!”
each person individually could have preferences nothing like U. The average of {1,1,1,1,1,1,1,1,1,1000} is about 100, but no individual number is anywhere near 100.
Deviations from utilility maximization (i.e. irrationality) will likely cancel out en masse.
Furthermore, economists will generally model some small part of human behaviour—e.g. purchasing tradeoffs in a particular domain—but I have never seen an economist model the entire human preference set. This is probably because it is too complicated for a team of expert economists to write down—never mind the poor individual concerned.
Good point, especially when it comes to markets. You can have a lot of people acting in predictably irrational ways, and a few people who see an inefficiency and make large sums of money off of it, and the net result is a quite rational market.
Average of large number of functions that look nothing like U has little reason to look much like U. The fact that something like U turns out repetitively needs an explanation.
It’s true that usually only a small portion of human behaviour is usually modeled at time, but utility maximization is composable, so you can take every single domain where utility maximization works, and compose it into one big utility maximization model—mathematically it should work (with some standard assumptions about types of error we have in small domain models, assumptions which might be false).
Sure! I don’t doubt this at all. I’m not saying that you cannot in principle build some humongous utility function that is a justifiable fit to what I, a particular human, want. BUT the point is that it isn’t feasible in practise—hence my statement in the original post:
What I was trying to do was more trying to figure out rough approximation of my utility function descriptively, to see if any of my actions are extremely irrational—like wasting too much time/money on something I care about very little, or not spending some time/money on something I care about a lot.
OK, but then the question is how do you approximate a mathematical function from a set X to R, especially when your biggest problem is not being able to enumerate the elements of X? If you miss out most of the elements of X, then no possible assignment of numbers to those that you do include will constitute a good approximation to the function.
I like this idea, though.
Approximation is likely to be a list of “I value event X relative to default state at Y utilons”, following economic tradition of focusing on the marginal. Skipping events from this list doesn’t affect comparisons between events on the list.
But what will you do with your incomplete list:
I value event make $x relative to default state at log(x) utilons
I value event play computer game relative to default state at 30 utilons
I value event marry woman of my dreams relative to default state at 1600 utilons
…
once you’ve compiled it? what do you do with the “utilon” numbers?
Next I look at utilons to costs ratios, and do more of things which result in events with high ratios, and less of things which result in events with low ratios.
By the way, as the function is marginal, value of money will be approximately linear, extra $100 is worth pretty much 100 times more than extra $1, it only breaks down on very large $s that significantly affect your net worth.