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In markets such as digital advertising auctions, bidders want to maximize value rather than payoff. This is different to the utility functions typically assumed in auction theory and leads to different strategies and outcomes. We refer to bidders who maximize value as value bidders. While simple single-object auction formats are truthful, standard multi-object auction formats allow for manipulation. It is straightforward to show that there cannot be a truthful and revenue-maximizing deterministic auction mechanism with value bidders and general valuations. Approximation has been used as a means to achieve truthfulness, and we study which approximation ratios we can get from truthful approximation mechanisms. We show that the approximation ratio that can be achieved with a deterministic and truthful approximation mechanism with $n$ bidders and $m$ items cannot be higher than 1/n for general valuations. For randomized approximation mechanisms there is a framework with a ratio of O(sqrt(m)). We provide better ratios for environments with restricted valuations.
Classic mechanism design often assumes that a bidders action is restricted to report a type or a signal, possibly untruthfully. In todays digital economy, bidders are holding increasing amount of private information about the auctioned items. And due
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Course assignment is a wide-spread problem in education and beyond. Often students have preferences for bundles of course seats or course schedules over the week, which need to be considered. The problem is a challenging distributed scheduling task r