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Vidick, T. (Ed.)We study auctions for carbon licenses, a policy tool used to control the social cost of pollution. Each identical license grants the right to produce a unit of pollution. Each buyer (i.e., firm that pollutes during the manufacturing process) enjoys a decreasing marginal value for licenses, but society suffers an increasing marginal cost for each license distributed. The seller (i.e., the government) can choose a number of licenses to put up for auction, and wishes to maximize the societal welfare: the total economic value of the buyers minus the social cost. Motivated by emission license markets deployed in practice, we focus on uniform price auctions with a price floor and/or price ceiling. The seller has distributional information about the market, and their goal is to tune the auction parameters to maximize expected welfare. The target benchmark is the maximum expected welfare achievable by any such auction under truthtelling behavior. Unfortunately, the uniform price auction is not truthful, and strategic behavior can significantly reduce (even below zero) the welfare of a given auction configuration. We describe a subclass of “safeprice” auctions for which the welfare at any BayesNash equilibrium will approximate the welfare under truthtelling behavior. We then show that the better of a safeprice auction, or a truthful auction that allocates licenses to only a single buyer, will approximate the target benchmark. In particular, we show how to choose a number of licenses and a price floor so that the worstcase welfare, at any equilibrium, is a constant approximation to the best achievable welfare under truthtelling after excluding the welfare contribution of a single buyer.more » « less

Daskalakis, C ; Fishelson, M ; Lucier, B ; Syrgkanis, V ; Velusamy, S ( , 21st ACM Conference on Economics and Computation, EC 2020)null (Ed.)We identify the first static credible mechanism for multiitem additive auctions that achieves a constant factor of the optimal revenue. This is one instance of a more general framework for designing twopart tariff auctions, adapting the duality framework of Cai et al [CDW16]. Given a (not necessarily incentive compatible) auction format A satisfying certain technical conditions, our framework augments the auction with a personalized entry fee for each bidder, which must be paid before the auction can be accessed. These entry fees depend only on the prior distribution of bidder types, and in particular are independent of realized bids. Our framework can be used with many common auction formats, such as simultaneous firstprice, simultaneous secondprice, and simultaneous allpay auctions. If allpay auctions are used, we prove that the resulting mechanism is credible in the sense that the auctioneer cannot benefit by deviating from the stated mechanism after observing agent bids. If secondprice auctions are used, we obtain a truthful O(1)approximate mechanism with fixed entry fees that are amenable to tuning via online learning techniques. Our results for first price and allpay are the first revenue guarantees of nontruthful mechanisms in multidimensional environments; an open question in the literature [RST17].more » « less