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@InCollection(AMEC02-rsdr,
        author = "Paul S. A. Reitsma and Peter Stone and J\'{a}nos A. Csirik and Michael L. Littman",
        title="Self-enforcing Strategic Demand Reduction",
        booktitle="Agent Mediated Electronic Commerce {IV}: Designing Mechanisms and Systems",
        series="Lecture Notes in Artificial Intelligence",      
        publisher="Springer Verlag",
        volume=2531,
        pages="289--306",
        year="2002",
        abstract={
                  Auctions are an area of great academic and
                  commercial interest, from tiny auctions for toys on
                  eBay to multi-billion-dollar auctions held by
                  governments for resources or contracts.  Although
                  there has been significant research on auction
                  theory, especially from the perspective of auction
                  mechanisms, studies of autonomous bidding agents and
                  their interactions are relatively few and recent.
                  This paper examines several autonomous agent bidding
                  strategies in the context of FAucS, a faithful
                  simulation of a complex FCC spectrum auction.  We
                  introduce punishing randomized strategic demand
                  reduction (PRSDR), a novel bidding strategy by
                  which bidders can partition available goods in a
                  mutually beneficial way without explicit inter-agent
                  communication.  When all use PRSDR, bidders obtain
                  significantly better results than when using a
                  reasonable baseline approach.  The strategy
                  automatically detects and punishes non-cooperating
                  bidders to achieve robustness in the face of agent
                  defection, and performs well under alternative
                  conditions.  The PRSDR strategy is fully implemented
                  and we present detailed empirical results.
        },
)