You don’t need to predict the futures and evaluate the ultimate utilities of having different sums of money to switch in monty hall problem thanks to having been taught statistics, though.
I have posted here before that calculating real utility numbers for comparison is quite silly exercise. One can do a lot better by calculating the results of comparison—e.g. by comparing the futures against each other side to side—effectively evaluating just the expected difference in the utility, only to the point where the sign is known (note that when you start bolting heuristic improvements onto this approach to utility maximization you get improved behaviour that is not necessarily consistent with utility maximization).
With regards to flying to NY scenario, the expected utility difference is measurable in micro-deaths, i.e. is very small, and if there’s no short-cut strategic values to maximize instead of the utility (such as money) one needs excessively precise calculations to decide on this action. Meaning that one could just as well do as one wishes. It’s splitting hair really.
You don’t need to predict the futures and evaluate the ultimate utilities of having different sums of money to switch in monty hall problem thanks to having been taught statistics, though.
I am not sure what you are saying here. What I am saying is that it is impossible to tell how much more utility you assign to world states where you own a car versus world states where you own a goat. You don’t even know if you wouldn’t be happier becoming a goatherder.
You can exchange car for goat plus other things, and you can’t exchange goat for car plus other things, so you can figure out that you’re better off getting the car. Maximizations of options for future decisions is a very solid heuristic that’s more local in time.
You can exchange car for goat plus other things, and you can’t exchange goat for car plus other things...
You seem to be moving the problem onto another level by partially grounding utility in material goods, respectively money, that can be exchanged for whatever it is that you really want.
I am much less troubled by expected utility maximization if utility is grounded in an empirical measure. The problem is, what measure are you going to use?
In a well-defined thought experiment like the Monty Hall problem it is relatively clear that a car would be the better choice because it can be exchanged for more of the other outcome. But in practice the problem is that there always are nearly infinitely many variables, actions and outcomes. It is hardly the best choice to maximize the measure that bears the label “utility” by taking part in a game show. So the question about the practical applicability of expected utility maximization, even as an approximation that deserves it’s name, remains.
Anyway, once you defined utility in terms of an empirical measure you solved a lot of problems that I referred to in original post. But this opens another can of worms. Whether you define utility in terms of happines or in terms of money doesn’t matter. You will end up maximizing the most useful quantity, e.g. computational resources. In other words, you’ll be forced to ignore your complex values to maximize that which most closely resembles the definition of utility in terms of an empirical measure, that which in exchange can / could be transformed into / exchanged / traded for all other quantities.
Well the issue with ‘utility maximization’ is that people instantly think of some real valued number that is being calculated, compared, etc. That’s not how it can possibly work in practice. In practice, you have unknowns; but you don’t always need to assign defined numerical values to unknowns to compare expressions involving unknowns.
In the case of money, having more money results in no lower future utility than having less money, because in the future there’s option to give up the money should they be found harmful—that’s almost independent of how the utility function is defined.
Actually, think of chess as example. Final utility values are win, tie, and loss. A heuristic that all chess players use is to maximize the piece disbalance—have more pieces than opponent, better located perhaps, etc. - in the foreseeable future, if they can’t foresee the end of game. This works for many games other than chess, which have different win conditions.
I’ve had a fair ammount of experience with goats. Trust me, you want none of it. Awful creatures. Go with the car. Or a bicycle, if you live in a place where they’re practical. Or jumping stilts, if you want to travel in style. Really anything but goats.
You don’t need to predict the futures and evaluate the ultimate utilities of having different sums of money to switch in monty hall problem thanks to having been taught statistics, though.
I have posted here before that calculating real utility numbers for comparison is quite silly exercise. One can do a lot better by calculating the results of comparison—e.g. by comparing the futures against each other side to side—effectively evaluating just the expected difference in the utility, only to the point where the sign is known (note that when you start bolting heuristic improvements onto this approach to utility maximization you get improved behaviour that is not necessarily consistent with utility maximization).
With regards to flying to NY scenario, the expected utility difference is measurable in micro-deaths, i.e. is very small, and if there’s no short-cut strategic values to maximize instead of the utility (such as money) one needs excessively precise calculations to decide on this action. Meaning that one could just as well do as one wishes. It’s splitting hair really.
I am not sure what you are saying here. What I am saying is that it is impossible to tell how much more utility you assign to world states where you own a car versus world states where you own a goat. You don’t even know if you wouldn’t be happier becoming a goatherder.
You can exchange car for goat plus other things, and you can’t exchange goat for car plus other things, so you can figure out that you’re better off getting the car. Maximizations of options for future decisions is a very solid heuristic that’s more local in time.
You seem to be moving the problem onto another level by partially grounding utility in material goods, respectively money, that can be exchanged for whatever it is that you really want.
I am much less troubled by expected utility maximization if utility is grounded in an empirical measure. The problem is, what measure are you going to use?
In a well-defined thought experiment like the Monty Hall problem it is relatively clear that a car would be the better choice because it can be exchanged for more of the other outcome. But in practice the problem is that there always are nearly infinitely many variables, actions and outcomes. It is hardly the best choice to maximize the measure that bears the label “utility” by taking part in a game show. So the question about the practical applicability of expected utility maximization, even as an approximation that deserves it’s name, remains.
Anyway, once you defined utility in terms of an empirical measure you solved a lot of problems that I referred to in original post. But this opens another can of worms. Whether you define utility in terms of happines or in terms of money doesn’t matter. You will end up maximizing the most useful quantity, e.g. computational resources. In other words, you’ll be forced to ignore your complex values to maximize that which most closely resembles the definition of utility in terms of an empirical measure, that which in exchange can / could be transformed into / exchanged / traded for all other quantities.
Well the issue with ‘utility maximization’ is that people instantly think of some real valued number that is being calculated, compared, etc. That’s not how it can possibly work in practice. In practice, you have unknowns; but you don’t always need to assign defined numerical values to unknowns to compare expressions involving unknowns.
In the case of money, having more money results in no lower future utility than having less money, because in the future there’s option to give up the money should they be found harmful—that’s almost independent of how the utility function is defined.
Actually, think of chess as example. Final utility values are win, tie, and loss. A heuristic that all chess players use is to maximize the piece disbalance—have more pieces than opponent, better located perhaps, etc. - in the foreseeable future, if they can’t foresee the end of game. This works for many games other than chess, which have different win conditions.
I’ve had a fair ammount of experience with goats. Trust me, you want none of it. Awful creatures. Go with the car. Or a bicycle, if you live in a place where they’re practical. Or jumping stilts, if you want to travel in style. Really anything but goats.