Two Topics in Online Auctions

This thesis studies two operations management topics in online auctions, and is di-vided into two parts.Motivated by the increasing use of ShopBots to scan Internet auctions, the firstpart of the thesis analytically examines whether or not two competing auctioneersselling the same commodity should share, or pool, some or all of their bidders. Un-der pooling, the bidding population is represented by three compartments: biddersdedicated to auction 1, bidders dedicated to auction 2, and pooled bidders partici-pating in both auctions simultaneously. Under a bidder strategy shown to induce aBayesian equilibrium, a closed form expression for the auctioneers’ expected revenueunder pooling is found, and pooling is recommended where it produces a greater ex-pected revenue than no pooling (i.e., our objective is revenue maximization). Poolingis generally found to be beneficial as long as the two auctions are not too asymmet-ric and the underlying valuation distribution has certain concavity characteristics.Asymptotic order statistic arguments are used where explicit characterizations areintractable.The second part of the thesis considers a manufacturer who uses a reverse, orprocurement, auction to determine which supplier will be awarded a contract. Eachbid consists of a price and a set of non-price attributes (e.g., quality, lead time).The manufacturer is assumed to know the suppliers’ cost functions (in terms of thenon-price attributes). We analyze how the manufacturer chooses a scoring rule (i.e.,a function that ranks the bids in terms of the price and non-price attributes) thatattempts to maximize his own utility. Under the assumption that suppliers submittheir myopic best-response bids (i.e., they choose their minimum-cost bid to achieveany given score), our proposed scoring rule indeed maximizes the manufacturer’sutility within the open-ascending format. The analysis reveals connections betweenthe manufacturer’s utility maximization problem and various geometric aspects of themanufacturer’s utility and the suppliers’ cost functions.