Inference from Auction Prices Econometric inference allows an analyst to back out the values of agents in a mechanism from the rules of the mechanism and bids of the agents. This paper proposes the problem of inferring the values of agents in a mechanism from the social choice function implemented by the mechanism and the perunit prices paid by the agents (the agent bids are not observed). For singledimensional agents, this inference problem is a multidimensional inversion of the payment identity and is feasible only if the payment identity is uniquely invertible. The inversion is unique for singleunit proportional weights social choice functions (common, for example, in bandwidth allocation); and its inverse can be found efficiently. This inversion is not unique for social choice functions that exhibit complementarities.

Revenue maximization in a Bayesian double auction market Abstract   We study double auction market design where the market maker wants to maximize its total revenue by buying low from the sellers and selling high to the buyers. We consider a Bayesian setting where buyers and sellers have independent probability distributions on the values of products on the market.  For the simplest setting, each seller has one kind of indivisible good with a bounded (integer) amount that can be sold to a buyer, who may demand a bounded number of copies. We develop a maximum mechanism for the market maker to maximize its own revenue.  For the more general case where each selleru0027s product may be different, we consider a number of variants in terms of constraints on supplies and demands. For each of them, we develop a polynomial time computable truthful mechanism for the market maker to achieve a revenue at least a constant  xcexb1  times the revenue of any other truthful mechanism.