The optimizer’s curse

The above sketch shows a decision tree. The circles are uncertainty nodes and the squares are decision nodes. Read the tree from left to right: to start, there is uncertainty of which of the strata i=1,…,I you will be in. In any given stratum, you will have to decide between options 1 and 2, and for each of these decision options there is uncertainty about the payoff. The goals are: (a) Conditional on the stratum, pick the best decision. This is the local decision problem. (b) Averaging over the strata, evaluate the expected value of the tree, that is, the expected value under an optimal decision analysis given the uncertainty. The challenge is that you don’t know which internal decision is best, because there is uncertainty about the payoffs. The “optimizer’s curse” is that if, for each stratum in step (a), you make the best decision given available information–that is, you estimate the expected payoff under each of the two decision options and then pick the the one whose expected…