Even assuming that can be solved in unit time, a literal interpretation of Equation 4 says we'll need to solve this optimization problem for an exponential number of cost vectors (or even more if the probability distributions are continuous). kearns99b kearns99b showed that values of partially observable Markov decision processes could be estimated accurately by sampling trajectories instead of exactly computing sums. littman00 littman00 did the same for stochastic satisfiability expressions. Applying this idea to Equation 4 leads to the following algorithm.

- Generate a set of vectors of closing costs according to the product distribution .
- For each of these samples, calculate
as
defined above and average the results, resulting in the approximation

A remaining challenge in evaluating Equation 6 is computing the real-valued bid that maximizes the value. Note that we want to buy item precisely at those closing prices for which the value of having the item (minus its cost) exceeds the value of not having the item; this maximizes profit. Thus, to make a positive profit, we are willing to pay up to, but not more than, the difference in value of having the item and not having the item.

Formally,
let be the vector
of current holdings and be the holdings modified to reflect
winning item . Let
, the optimal
set of purchases assuming item was won, and
the optimal set of purchases assuming otherwise
(except in cases of ambiguity, we write simply and for
and respectively). We want to select to
achieve the equivalence

achieves the equivalence desired in Equation 7, as can be verified by substitution, and therefore bidding the average difference between holding and not holding the item maximizes the value.