Was reading Rust’s paper on engine replacement. i was thinking this is a great paper to test whether dynamic programming works for actual human decision makers.
How good is stochastic dynamic programming in explaining the decision to replace a bus engine? the best metric it has is an estimated hazard function, that gives probability of replacement as a function of mileage. he also has confidence intervals around the hazard function.
how does this stand up to the data? can dynamic programming predict zucker’s behavior?
rust doesnt directly show this. he does estimate an empirical (non-parametric) estimate of the hazard function (fig 3). In another figure, fig 5, we have the 1 stnd dev bands, as well as the 95% CI bands. unfortunately, its not superimposed, but i think that the CI bands capture some of the empirical hazard, but not all…
most of the observations are around the 200k mileage, the mean. the total number of buses available is 162, but they at the end look at only a subset of that. i wonder how good the prediction is at the mean? not as good, but rust doesn’t provide the guidance at all as to how well…
the empirical hazard becomes erratic high levels of mileage, coz of a lack of observations. at these levels of mileage, there are only a few obs, which are either replacement or not. meanwhile the estimated hazard levels out at high levels (non-myopic) levels off at 7%.
here’s the interesting part, the lack of observations makes it hard to determine which cost function mr zurcher was using. after speaking with zurcher, rust chooses a linear specification.
this is unfortunate coz, if zurcher is not around, how do we know which specification is correct? how do we choose?
A specific question, rust says the method is ‘efficient’. does he mean minimum variance for a given sample size? i’m not sure this is proven, altho i’m willing to believe it.
Posted by outinfour
Posted by outinfour
Posted by outinfour