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To match or not to match: Economics of cookie matching in online advertising
"... Modern online advertising increasingly relies on the availability of user tracking technology called cookiematching to increase efficiency in ad allocations. Web publishers today use this technology to share information about the websites a user has visited, making it possible to target advertisemen ..."
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Cited by 7 (2 self)
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Modern online advertising increasingly relies on the availability of user tracking technology called cookiematching to increase efficiency in ad allocations. Web publishers today use this technology to share information about the websites a user has visited, making it possible to target advertisements to users based on their prior history. This begs the question: do publishers (who are competitors for advertising money) always have the incentive to share online information? Intuitive arguments as well as anecdotal evidence suggest that sometimes a premium publisher might suffer from information sharing through an effect called information leakage: by sharing user information with the advertiser, the advertiser will be able to target the same user elsewhere on cheaper publishers, leading to a dilution of the value of the supply on the premium publishers. The goal of this paper is to explore this aspect of online information sharing. We show that when advertisers are homogeneous (i.e., they value the users similarly, up to a constant multiple), in equilibrium, the publishers always agree about the benefits of cookiematching (i.e., either they all benefit, or they all suffer from it). We also analyze a simple model that exhibits how information leakage can help one publisher and harm the other when the advertisers are not homogeneous.
Constrained signaling in auction design
 In Proceedings of the 25th ACM Symposium on Discrete Algorithms (SODA
, 2014
"... We consider the problem of an auctioneer who faces the task of selling a good (drawn from a known distribution) to a set of buyers, when the auctioneer does not have the capacity to describe to the buyers the exact identity of the good that he is selling. Instead, he must come up with a constrained ..."
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Cited by 6 (4 self)
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We consider the problem of an auctioneer who faces the task of selling a good (drawn from a known distribution) to a set of buyers, when the auctioneer does not have the capacity to describe to the buyers the exact identity of the good that he is selling. Instead, he must come up with a constrained signalling scheme: a (non injective) mapping from goods to signals, that satisfies the constraints of his setting. For example, the auctioneer may be able to communicate only a bounded length message for each good, or he might be legally constrained in how he can advertise the item being sold. Each candidate signaling scheme induces an incompleteinformation game among the buyers, and the goal of the auctioneer is to choose the signaling scheme and accompanying auction format that optimizes welfare. In this paper, we use techniques from submodular function maximization and noregret learning to give algorithms for computing constrained signaling schemes for a variety of constrained signaling problems. 1
Constrained signaling for welfare and revenue maximization
, 2013
"... We consider the problem of an auctioneer who faces the task of selling a good (drawn from a known distribution) to a set of buyers, when the seller does not have the capacity to describe to the buyers the exact identity of the good that he is selling. Instead, he must come up with a constrained sign ..."
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Cited by 5 (2 self)
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We consider the problem of an auctioneer who faces the task of selling a good (drawn from a known distribution) to a set of buyers, when the seller does not have the capacity to describe to the buyers the exact identity of the good that he is selling. Instead, he must come up with a constrained signalling scheme: a (non injective) mapping from goods to signals, that satisfies the constraints of his setting. For example, the auctioneer may be able to communicate only a bounded length message for each good (equivalently, he may be constrained to using only a fixed number of signals in total). The auctioneer may also face additional exogenously imposed constrains on the signaling scheme – for example, he might be legally constrained to truthfully advertise the item being sold. Each candidate signaling scheme induces an incompleteinformation game among the buyers, and the goal of the mechanism designer is to choose the signaling scheme that optimizes either welfare or revenue. In this paper, we give algorithms for computing constrained signaling schemes, as well as hardness results, for both of these objectives for a variety of constrained signaling problems.
Ad Auctions with Data
"... The holy grail of online advertising is to target users with ads matched to their needs with such precision that the users respond to the ads, thereby increasing both advertisers’ and users’ value. The current approach to this challenge utilizes information about the users: their gender, their loc ..."
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The holy grail of online advertising is to target users with ads matched to their needs with such precision that the users respond to the ads, thereby increasing both advertisers’ and users’ value. The current approach to this challenge utilizes information about the users: their gender, their location, the websites they have visited before, and so on. Incorporating this data in ad auctions poses an economic challenge: can this be done in a way that the auctioneer’s revenue does not decrease (at least on average)? This is the problem we study in this paper. Our main result is that in Myerson’s optimal mechanism, for a general model of data in auctions, additional data leads to additional expected revenue. In the context of ad auctions we show that for the simple and common mechanisms, namely second price auction with reserve prices, there are instances in which additional data decreases the expected revenue, but this decrease is by at most a small constant factor under a standard regularity assumption.
The asymmetric matrix partition problem
, 2012
"... An instance of the asymmetric matrix partition problem consists of a matrix A ∈ R n×m + and a probability distribution p over its columns. The goal is to find a partition scheme that maximizes the resulting partition value. A partition scheme S = {S1,..., Sn} consists of a partition Si of [m] for e ..."
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An instance of the asymmetric matrix partition problem consists of a matrix A ∈ R n×m + and a probability distribution p over its columns. The goal is to find a partition scheme that maximizes the resulting partition value. A partition scheme S = {S1,..., Sn} consists of a partition Si of [m] for each row i of the matrix. The partition Si can be interpreted as a smoothing operator on row i, which replaces the value of each entry in that row with the expected value in the partition subset that contains it. Given a scheme S that induces a smoothed matrix A ′ , the partition value is the expected maximum column entry of A ′. We establish that this problem is already APXhard for the seemingly simple setting in which A is binary and p is uniform. We then demonstrate that a constant factor approximation can be achieved in most cases of interest. Later on, we discuss the symmetric version of the problem, in which one must employ an identical partition for all rows, and prove that it is essentially trivial. Our matrix partition problem draws its interest from several applications like broad matching in sponsored search advertising and information revelation in market settings. We conclude by discussing the latter application in depth.
On the Hardness of Signaling
, 2014
"... There has been a recent surge of interest in the role of information in strategic interactions. Much of this work seeks to understand how the realized equilibrium of a game is influenced by uncertainty in the environment and the information available to players in the game. Lurking beneath this lite ..."
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There has been a recent surge of interest in the role of information in strategic interactions. Much of this work seeks to understand how the realized equilibrium of a game is influenced by uncertainty in the environment and the information available to players in the game. Lurking beneath this literature is a fundamental, yet largely unexplored, algorithmic question: how should a “market maker ” who is privy to additional information, and equipped with a specified objective, inform the players in the game? This is an informational analogue of the mechanism design question, and views the information structure of a game as a mathematical object to be designed, rather than an exogenous variable. We initiate a complexitytheoretic examination of the design of optimal information structures in general Bayesian games, a task often referred to as signaling. We focus on one of the simplest instantiations of the signaling question: Bayesian zerosum games, and a principal who must choose an information structure maximizing the equilibrium payoff of one of the players. In this setting, we show that optimal signaling is computationally intractable, and in some cases hard to approximate, assuming that it is hard to recover a planted clique from an ErdősRényi random graph. This is despite the fact that equilibria in these games are computable in polynomial time, and therefore suggests that the hardness of optimal signaling is a distinct phenomenon from the hardness of equilibrium computation. Necessitated by the nonlocal nature of information structures, enroute to our results we prove an “amplification lemma ” for the planted clique problemwhichmay be of independent interest. Specifically, we show that even if we plant many cliques in an ErdősRényi random graph, so much so that most nodes in the graph are in some planted clique, recovering a constant fraction of the planted cliques is no easier than the traditional planted clique problem. 1
Information disclosure as a means to security
 in Proc. International Conference on Autonomous Agents and Multiagent Systems, 2015
"... In this paper we present a novel Stackelbergtype model of security domains: Security Assets aSsignment with Information disclosure (SASI). The model combines both the features of the Stackelberg Security Games (SSGs) model and of the Bayesian Persuasion (BP) model. More specifically, SASI includ ..."
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In this paper we present a novel Stackelbergtype model of security domains: Security Assets aSsignment with Information disclosure (SASI). The model combines both the features of the Stackelberg Security Games (SSGs) model and of the Bayesian Persuasion (BP) model. More specifically, SASI includes: a) an uncontrolled, exogenous security state that serves as the Defender’s private information; b) multiple security assets with nonaccumulating, targetlocal defence capability; c) a proactive, verifiable and public, unidirectional information disclosure channel from the Defender to the Attacker. We show that SASI with a nondegenerate information disclosure can be arbitrarily more efficient, than a “silent ” Stackelberg assets allocation. We also provide a linear program reformulation of SASI that can be solved in polynomial time in SASI parameters. Furthermore, we show that it is possible to remove one of SASI parameters and, rather than require it as an input, recover it by computation. As a result, SASI becomes highly scalable.
Posted Prices Exchange for Display Advertising Contracts
 In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI’13
, 2013
"... Abstract We propose a new market design for display advertising contracts, based on posted prices. Our model and algorithmic framework address several major challenges: (i) the space of possible impression types is exponential in the number of attributes, which is typically large, therefore a compl ..."
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Abstract We propose a new market design for display advertising contracts, based on posted prices. Our model and algorithmic framework address several major challenges: (i) the space of possible impression types is exponential in the number of attributes, which is typically large, therefore a complete price space cannot be maintained; (ii) advertisers are usually unable or reluctant to provide extensive demand (willingnesstopay) functions, (iii) the levels of detail with which supply and demand are specified are often not identical.
Constrained Signaling for Welfare and Revenue
"... We consider auction settings where the seller is constrained in the amount and nature of information he may reveal about the good being sold. This is encountered, for example, in online advertising auctions, where communicating precise details of every viewer to interested advertisers is impractical ..."
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We consider auction settings where the seller is constrained in the amount and nature of information he may reveal about the good being sold. This is encountered, for example, in online advertising auctions, where communicating precise details of every viewer to interested advertisers is impractical, costly, and possibly socially undesirable. We initiate the study of constrained signaling in such settings, where a seller must choose which information to reveal subject to exogenous constraints on the signaling policy. We consider a seller employing the secondprice auction, and present algorithms and hardness results for approximating the welfare and revenue maximizing signaling policies under a variety of constraints.