Optimizing auctioneer's revenues in expanding multi-unit auctions

Abstract

In this study we provide a strategy that maximizes the expected revenues of the auctioneer in an expanding multi-unit auction. We model the auction process as a state graph in which nodes are auction states and edges are transitions. With this model, finding the optimal strategy is equivalent to solving a search problem on the state graph. We prove that the search problem to be solved, although seemingly exponentially complex, is actually linearly bounded. Based on this result, we introduce an informed strategy that optimizes the auctioneer's revenue.