[**interviewer**]{} evaluates $N$ [**candidates**]{} one by one. After each interview, the interviewer ranks the current candidate against all of the candidates interviewed so far, and decides whether to [**accept**]{} the current current candidate (ending the game) or to [**reject**]{} the current candidate (in which case, they cannot be recalled later). The goal of the game is to hire the best candidate out of $N$. It turns out that the optimal strategy is to reject an initial set of candidates, of size $N/e$ when $N$ is large, and use them as a training set by hiring the next candidate who is better than all of them (or the last candidate if no subsequent candidate is better). The probability of hiring the best candidate out of $N$ with this strategy also approaches $1/e$.

The classical model assumes that all $N!$ interview rank orders are equally likely, which we believe is mathematically expedient but unrealistic. Over the period that the player is conducting the $N$ interviews, there may exist extrinsic trends in the candidate pool. Also, as the interviewer ranks the candidates at each step, they acquire information about the domain that allows them to hone the pool to include more relevant candidates. 