The section on experimental studies touches on it briefly, but it's important to note that costs involved in the selection process are not considered in the standard construction of the problem.
From wikipedia:
> In large part, this work has shown that people tend to stop searching too soon. This may be explained, at least in part, by the cost of evaluating candidates.
and then
> For example, when trying to decide at which gas station to stop for gas, people might not search enough before stopping. If true, then they would tend to pay more for gas than they might had they searched longer.
So you might pay more for gas if you don't use the optimal strategy, but waste more money in fuel costs searching for the cheaper price than you save buy purchasing at that price.
Perhaps most interestingly, there is a formulation of the problem that requires a decision to be made within a certain time period, from an unknown number of candidates who arrive over that time period. If you know (or can estimate) the arrival times of the candidates, you can use a very similar strategy to achieve optimal results.
Essentially, wait until you have seen 1/e of expected candidates in the time frame you have allowed for (based on the arrival density function you know or estimated) then pick the next best option.
This puts a limit on the amount of searching you do, and so provides an optimal strategy with a bounded limit on how long you search for; it provides a bound on the cost involved in the search assuming the cost is related to time taken.
In the searching for gas example, you could use this strategy if you knew roughly how often you pass a gas station, and how long you are willing to search for.
From wikipedia:
> In large part, this work has shown that people tend to stop searching too soon. This may be explained, at least in part, by the cost of evaluating candidates.
and then
> For example, when trying to decide at which gas station to stop for gas, people might not search enough before stopping. If true, then they would tend to pay more for gas than they might had they searched longer.
So you might pay more for gas if you don't use the optimal strategy, but waste more money in fuel costs searching for the cheaper price than you save buy purchasing at that price.
Perhaps most interestingly, there is a formulation of the problem that requires a decision to be made within a certain time period, from an unknown number of candidates who arrive over that time period. If you know (or can estimate) the arrival times of the candidates, you can use a very similar strategy to achieve optimal results.
Essentially, wait until you have seen 1/e of expected candidates in the time frame you have allowed for (based on the arrival density function you know or estimated) then pick the next best option.
This puts a limit on the amount of searching you do, and so provides an optimal strategy with a bounded limit on how long you search for; it provides a bound on the cost involved in the search assuming the cost is related to time taken.
In the searching for gas example, you could use this strategy if you knew roughly how often you pass a gas station, and how long you are willing to search for.