Discounted i.i.d. rewards force prophet inequality competitive ratios down to 1/2, as hard as the non-i.i.d. case, with matching upper and lower bounds via calibrated single-quantile thresholds.
Beating 1-1/e for Ordered Prophets
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abstract
Hill and Kertz studied the prophet inequality on iid distributions [The Annals of Probability 1982]. They proved a theoretical bound of $1-\frac{1}{e}$ on the approximation factor of their algorithm. They conjectured that the best approximation factor for arbitrarily large n is $\frac{1}{1+1/e} \approx 0.731$. This conjecture remained open prior to this paper for over 30 years. In this paper we present a threshold-based algorithm for the prophet inequality with n iid distributions. Using a nontrivial and novel approach we show that our algorithm is a 0.738-approximation algorithm. By beating the bound of $\frac{1}{1+1/e}$, this refutes the conjecture of Hill and Kertz. Moreover, we generalize our results to non-iid distributions and discuss its applications in mechanism design.
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I.i.d. Prophet Inequalities with Discounted Rewards: As Hard as the Non-i.i.d. Case
Discounted i.i.d. rewards force prophet inequality competitive ratios down to 1/2, as hard as the non-i.i.d. case, with matching upper and lower bounds via calibrated single-quantile thresholds.