We investigate the “generalized second-price ” (GSP) auction, a new mechanism used by search engines to sell online advertising. Although GSP looks similar to the Vickrey-Clarke-Groves (VCG) mechanism, its properties are very different. Unlike the VCG mechanism, GSP generally does not have an equilibrium in dominant strategies, and truth-telling is not an equilibrium of GSP. To analyze the properties of GSP, we describe the generalized English auction that corresponds to GSP and show that it has a unique equilibrium. This is an ex post equilibrium, with the same payoffs to all players as the dominant strategy equilibrium of VCG. (JEL D44, L81, M37) This paper investigates a new auction mechanism, which we call the “generalized secondprice” auction, or GSP. GSP is tailored to the unique environment of the market for online ads, and neither the environment nor the mechanism has previously been studied in the mechanism design literature. While studying the properties of a novel mechanism is often fascinating in itself, our interest is also motivated by the spectacular commercial success of GSP. It is the dominant transaction mechanism in a large and rapidly growing industry. For example, Google’s total revenue in 2005 was $6.14 billion. Over 98 percent of its revenue came from GSP auctions. Yahoo!’s total revenue in 2005 was $5.26 billion. A large share of Yahoo!’s revenue is derived from sales via GSP auctions. It is believed that over half of Yahoo!’s revenue is derived from sales via GSP auctions. As of May 2006, the combined market capitalization of these companies exceeded $150 billion. Let us briefly describe how these auctions work. When an Internet user enters a search

This paper concerns the labor market for medical interns and residents and how it has evolved from the beginning of the century to the present time. The paper will discuss briefly the history of the various

I analyze the equilibria of a game based on the ad auction used by Google and Yahoo. This auction is closely related to the assignment game studied by Shapley-Shubik, Demange-Gale-Sotomayer and Roth-Sotomayer. However, due to the special structure of preferences, the equilibria of the ad auction can be calculated explicitly and some known results can be sharpened. I provide some empirical evidence that the Nash equilibria of the position auction describe the basic properties of the prices observed in Google's ad auction reasonably accurately.

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The allocation of discrete, complementm'y resources is a fundamental probleln in econolnics and of direct interest to e-colnlnerce applications. Combinatorial auctions account for complementarities by optimizing over offers expressed in terms of bundles. Progressive versions of combinatorial auctions alleviate the burden on bidders of expressing offers for all bundles of interest by providing interiln feedback based on partial sets of bids. Feedback in terms of hypothetical prices is particularly useful, as it directs bidders to,yard those bundles potentially yielding the greatest surplus. For a general class of discrete resource allocation problelns vith free disposal, we establish by construction the existence of competitive equilibriuln prices on bundles that support the efficient allocation. We introduce AkBA, a falnily of progressive auctions that use these equilibriuln bundle prices. We exalnine a particular instance of the family, called A1BA, and present SOlne elnpirical data on its perforlnance.

This paper introduces a new model of exchange: networks, rather than markets, of buyers and sellers. It begins with the empirically motivated premise that a buyer and seller must have a relationship, a “link, ” to exchange goods. Networks- buyers, sellers, and the pattern of links connecting them- are common exchange environments. This paper develops a methodology to study network structures and explains why agents may form networks. In a model that captures characteristics of a variety of industries, the paper shows that buyers and sellers, acting strategically in their own self-interests, can form the network structures that maximize overall

Recent developments in computer networks have driven the cost of distributing information virtually to zero, creating extraordinary opportunities for sharing product evaluations. We present pricing and subsidy mechanisms that operate through a computerized market and induce the efficient provision of evaluations. The mechanisms overcome three major challenges: first, evaluations, which are public goods, are likely to be underprovided; second, an inefficient ordering of evaluators may arise; third, the optimal quantity of evaluations depends on what is learned from the initial evaluations. Keywords: evaluations, information sharing, product quality, computer network, market (JEL D70, D83, H41, L15) 2 Subjective evaluations by others are a valuable tool for consumers who are choosing which products to buy or how to spend their time. For example, we read magazines devoted to product evaluation before purchasing cars and appliances. We ask our friends and read reviews by professional cr...

The Vickrey sealed bid auction occupies a central place in auction theory because of its efficiency and incentive properties. Implementing the auction requires the auctioneer to solve n + 1 optimization problems,where n is the number of bidders. In this paper we survey various environments (some old and some new) where the payments bidders make under the Vickrey auction correspond to dual variables in certain linear programs. Thus,in these environments,at most two optimization problems must be solved to determine the Vickrey outcome. Furthermore,primal-dual algorithms for some of these linear programs suggest ascending auctions that implement the Vickrey outcome.

Combinatorial auctions allow bidders to express complex valuations on bundles of items, and have been proposed in settings as diverse as the allocation of floor space in a new condominium building to individual units (Wired 2000) and the allocation of take-off and landing slots at airports (Smith, Forward). Many applications are described in Part V of this book. The promise of combinatorial auctions (CAs) is that they can allow bidders to better express their private information about preferences for different outcomes and thus enhance competition and market efficiency. Much effort has been spent on developing algorithms for the hard problem of winner determination once bids have been received (Sandholm, Chapter 14). Yet, preference elicitation has emerged as perhaps the key bottleneck in the real-world deployment of combinatorial auctions. Advanced clearing algorithms are worthless if one cannot simplify the bidding problem facing bidders. Preference elicitation is a p