When autonomous agents decide on their bidding strategies in real world auctions, they have a number of concerns that go beyond the models that are normally analyzed in traditional auction theory. Oftentimes, the agents have budget constraints and the auctions have a reserve price, both of which restrict the bids the agents can place. In addition, their attitude need not be risk-neutral and they may have uncertainty about the value of the goods they are buying. Some of these issues have been examined individually for single-unit sealed-bid auctions. However, in this paper, we extend this analysis to the multi-unit case, and also analyze the multi-unit sealed-bid auctions in which a combination of these issues are present, for unit-demand bidders. This analysis constitutes the main contribution of this paper. We then demonstrate the usefulness in practice of this analysis; we show in simulations that taking into account all these issues allows the bidders to maximize their utility. Furthermore, using this analysis allows a seller to improve her revenue, i.e. by selecting the optimal reserve price and auction format.
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