LAUSR

Stochastic multi-armed bandit (MAB) mechanisms are widely used in sponsored search auctions, crowdsourcing, online procurement, etc. Existing stochastic MAB mechanisms with a deterministic payment rule, proposed in the literature, necessarily suffer a regret of Ω ( T 2/3 ), where T is the number of time steps. This happens because the existing mechanisms consider the worst case scenario where the means of the agents’ stochastic rewards are separated by a very small amount that depends on T . We make, and, exploit the crucial observation that in most scenarios, the separation between the agents’ rewards is rarely a function of T . Moreover, in the case that the rewards of the arms are arbitrarily close, the regret contributed by such sub-optimal arms is minimal. Our idea is to allow the center to indicate the resolution, Δ , with which the agents must be distinguished. This immediately leads us to introduce the notion of Δ -Regret. Using sponsored search auctions as a concrete example (the same idea applies for other applications as well), we propose a dominant strategy incentive compatible (DSIC) and individually rational (IR), deterministic MAB mechanism, based on ideas from the Upper Confidence Bound (UCB) family of MAB algorithms. Remarkably, the proposed mechanism Δ -UCB achieves a Δ -regret of O ( log T ) $O(\log T)$ for the case of sponsored search auctions. We first establish the results for single slot sponsored search auctions and then non-trivially extend the results to the case where multiple slots are to be allocated.