AI

Oblivious Dynamic Mechanism Design

Abstract

Despite their better revenue and welfare guarantees for repeated auctions, dynamic mechanisms have not been widely adopted in practice. This is partly due to their computational and implementation complexity, and also due to their unrealistic use of forecasting for future periods. We address the above shortcomings and present a new family of dynamic mechanisms that are computationally efficient and do not use any distribution knowledge of future periods. Our contributions are three-fold:

  1. We present the first polynomial-time dynamic incentive-compatible and ex-post individually rational mechanism for multiple periods and for any number of buyers that is a constant approximation to the optimal revenue. Unlike previous mechanisms, we require no expensive pre-processing step and in each period we run a simple auction that is a combination of virtual value maximizing auctions.

  2. We introduce the concept of obliviousness in dynamic mechanism design. A dynamic mechanism is oblivious if the allocation and pricing rule at each period does not depend on the type distributions in future periods. Our mechanism is oblivious and guarantees a 5-approximation compared to the optimal mechanism that knows all the distributions in advance.

  3. We develop a framework for characterizing, designing, and proving lower bounds for dynamic mechanisms (oblivious or not). In addition to the aforementioned positive results, we use this characterization to show that no oblivious mechanism can produce a better-than-2 approximation to the mechanism that knows all the distributions.