Theses and Dissertations from UMD
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Item Learning and Robustness With Applications To Mechanism Design(2022) Curry, Michael Jeremiah; Dickerson, John P; Goldstein, Thomas; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The design of economic mechanisms, especially auctions, is an increasingly important part of the modern economy. A particularly important property for a mechanism is strategyproofness -- the mechanism must be robust to strategic manipulations so that the participants in the mechanism have no incentive to lie. Yet in the important case when the mechanism designer's goal is to maximize their own revenue, the design of optimal strategyproof mechanisms has proved immensely difficult, with very little progress after decades of research. Recently, to escape this impasse, a number of works have parameterized auction mechanisms as deep neural networks, and used gradient descent to successfully learn approximately optimal and approximately strategyproof mechanisms. We present several improvements on these techniques. When an auction mechanism is represented as a neural network mapping bids from outcomes, strategyproofness can be thought of as a type of adversarial robustness. Making this connection explicit, we design a modified architecture for learning auctions which is amenable to integer-programming-based certification techniques from the adversarial robustness literature. Existing baselines are empirically strategyproof, but with no way to be certain how strong that guarantee really is. By contrast, we are able to provide perfectly tight bounds on the degree to which strategyproofness is violated at any given point. Existing neural networks for auctions learn to maximize revenue subject to strategyproofness. Yet in many auctions, fairness is also an important concern -- in particular, fairness with respect to the items in the auction, which may represent, for instance, ad impressions for different protected demographic groups. With our new architecture, ProportionNet, we impose fairness constraints in addition to the strategyproofness constraints, and find approximately fair, approximately optimal mechanisms which outperform baselines. With PreferenceNet, we extend this approach to notions of fairness that are learned from possibly vague human preferences. Existing network architectures can represent additive and unit-demand auctions, but are unable to imposing more complex exactly-k constraints on the allocations made to the bidders. By using the Sinkhorn algorithm to add differentiable matching constraints, we produce a network which can represent valid allocations in such settings. Finally, we present a new auction architecture which is a differentiable version of affine maximizer auctions, modified to offer lotteries in order to potentially increase revenue. This architecture is always perfectly strategyproof (avoiding the Lagrangian-based constrained optimization of RegretNet) -- to achieve this goal, however, we need to accept that we cannot in general represent the optimal auction.Item Essays on Auction Design(2018) Yan, Haomin; Ausubel, Lawrence M; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation studies the design of auction markets where bidders are uncertain of their own values at the time of bidding. A bidder's value may depend on other bidders' private information, on total quantity of items allocated in the auction, or on the auctioneer's private information. Chapter 1 provides a brief introduction to auction theory and summarizes the main contribution of each following chapter. Chapter 2 of this dissertation extends the theoretical study of position auctions to an interdependent values model in which each bidder's value depends on its opponents' information as well as its own information. I characterize the equilibria of three standard position auctions under this information structure, including the Generalized Second Price (GSP) auctions, Vickrey-Clarke-Groves (VCG) auctions, and the Generalized English Auctions (GEA). I first show that both GSP and VCG auctions are neither efficient nor optimal under interdependent values. Then I propose a modification of these two auctions by allowing bidders to condition their bids on positions to implement efficiency. I show that the modified auctions proposed in this chapter are not only efficient, but also maximize the search engine's revenue. While the uncertainty of each bidder about its own value comes from the presence of common component in bidders’ ex-post values in an interdependent values model, bidders can be uncertain about their values when their values depend on the entire allocation of the auction and when their values depend on the auctioneer's private information. Chapter 3 of this dissertation studies the design of efficient auctions and optimal auctions in a license auction market where bidders care about the total quantity of items allocated in the auction. I show that the standard uniform-price auction and the ascending clock auction are inefficient when the total supply needs to be endogenously determined within the auction. Then I construct a multi-dimensional uniform-price auction and a Walrasian clock auction that can implement efficiency in a dominant strategy equilibrium under surplus-maximizing reserve prices and achieve optimal revenue under revenue-maximizing reserve prices. Chapter 4 of this dissertation analyzes an auctioneer's optimal information provision strategy in a procurement auction in which the auctioneer has private preference over bidders' non-price characteristics and bidders invest in cost-reducing investments before entering the auction. I show that providing more information about the auctioneer's valuation over bidders' non-price characteristics encourages those favored bidders to invest more and expand the distribution of values in the auction. Concealment is the optimal information provision policy when there are two suppliers.Item Self-regulation, productivity, and nonlinear pricing. Three essays on quality production in agricultural markets(2006-05-16) Zago, Angelo; Chambers, Robert G.; Agricultural and Resource Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation I analyze the quality choices of a group of producers. In the first essay I use mechanism design to study the interaction of asymmetric information and the democratic process in the quality choices of a group of heterogeneous producers facing an opportunity to gain from establishing a reputation for their quality products. I find an asymmetry in the possible equilibria between the high and the low quality majorities. The quality level provided by the group with a low quality majority is lower than the first best, and the minority producers get rents. With high quality majority, if demand and group conditions are favourable, the quality level provided by the group is higher than the first best and the minority's type left with rents. Otherwise, the quality level provided by the group is first best and no rents are left to the low-quality producers in the minority. The second essay proposes a methodology to measure the characteristics of intermediate products when quality is multidimensional. It uses a general representation of the multioutput technology via directional distance functions and constructs quality indicators based on differences. The quality indicators may be used to evaluate firms' output taking into account the whole set of quality attributes. I explore the relationships among the different quality attributes and the yields by a systematic investigation of the disposability properties of the technology. In addition, I show how aggregate quality may vary with the production level. The third essay designs an optimal payment system for a group of producers implementing it empirically. In the essay I show how to implement the first best through higher prices for better quality commodities, deriving the optimal pricing schedule. I take into account producers' heterogeneity by modelling inefficiency and illustrating how technical efficiency interacts with producers' ability to produce output for a given level of inputs and hence affects revenues. The technology and the technical efficiency of producers are then estimated with a stochastic production function model. The estimation results are then used to simulate the pricing scheme.Item A Least-Cost Mechanism to Achieve Agricultural Income and Conservation Targets under Asymmetric Information(2004-11-23) Sheriff, Glenn David; Chambers, Robert G; Agricultural and Resource Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Two policy goals dominate United States' agricultural programs: voluntary land retirement for environmental purposes and countercyclical income support. Traditionally, these goals have been pursued with separate policies. This policy separation is efficient with perfect information regarding farm productivity. A more realistic assumption, however, is that farmers have better information regarding their own productivity than the government. The focus of the dissertation is to analyze least cost agricultural policy with this type of asymmetric information. I first use a mechanism design framework to show that it is optimal to have a combined income support-land retirement program rather than separate programs. For land retirement, farmers have an incentive to overstate productivity in order to receive a higher rental payment. For income support, farmers have an incentive to understate productivity to receive a higher income support payment. With high output prices, the first effect dominates. With low prices, the second dominates. Farmers' ability to use private information to their advantage increases the cost to the government of reaching its targets. If contract commitment takes place when output prices are uncertain, the two incentives can countervail each other, reducing the cost of the policy to the government. In the second part of the dissertation, I extend the literature by showing how one can implement the policy using actual data. I conduct a numerical simulation to determine the exact payment and land set aside for each farmer. To calibrate the simulation, I apply stochastic frontier analysis to a data set of US farmers. I thus obtain consistent estimates of the key determinants of the contracts: the farm profit function and the probability distribution of profitability levels across the sector. Simulation results show that unlike current programs, the least cost contract is likely to involve pooling. Farmers with different profitability levels receive identical expected payments for idling identical acreage. The countervailing incentives created by the least-cost policy almost eliminate the information advantage of farmers, significantly reducing cost relative to current programs.