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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 to legal or ethical concerns, they would demand to reveal partial but truthful information, as opposed to report untrue signal or misinformation. To accommodate such bidder behaviors in auction design, we propose and study a novel mechanism design setup where each bidder holds two kinds of information: (1) private emph{value type}, which can be misreported; (2) private emph{information variable}, which the bidder may want to conceal or partially reveal, but importantly, emph{not} to misreport. We show that in this new setup, it is still possible to design mechanisms that are both emph{Incentive and Information Compatible} (IIC). We develop two different black-box transformations, which convert any mechanism $mathcal{M}$ for classic bidders to a mechanism $mathcal{M}$ for strategically reticent bidders, based on either outcome of expectation or expectation of outcome, respectively. We identify properties of the original mechanism $mathcal{M}$ under which the transformation leads to IIC mechanisms $mathcal{M}$. Interestingly, as corollaries of these results, we show that running VCG with expected bidder values maximizes welfare whereas the mechanism using expected outcome of Myersons auction maximizes revenue. Finally, we study how regulation on the auctioneers usage of information may lead to more robust mechanisms.
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
One of the Multi-Agent Systems that is widely used by various government agencies, buyers and sellers in a market economy, in such a manner so as to attain optimized resource allocation, is the Combinatorial Auctioning System (CAS). We study another
We study the problem of selling a good to a group of bidders with interdependent values in a prior-free setting. Each bidder has a signal that can take one of $k$ different values, and her value for the good is a weakly increasing function of all the
We investigate revenue guarantees for auction mechanisms in a model where a distribution is specified for each bidder, but only some of the distributions are correct. The subset of bidders whose distribution is correctly specified (henceforth, the gr
We study a central problem in Algorithmic Mechanism Design: constructing truthful mechanisms for welfare maximization in combinatorial auctions with submodular bidders. Dobzinski, Nisan, and Schapira provided the first mechanism that guarantees a non