Google’s Auction for TV Ads

Nisan, Noam; Bayer, Jason; Chandra, Deepak; Franji, Tal; Gardner, R. L.; Matias, Yossi; Rhodes, Neil; Seltzer, Misha; Tom, Danny; Varian, Hal R.; Zigmond, Dan

doi:10.1007/978-3-642-02930-1_26

Cited by 32 publications

(15 citation statements)

References 10 publications

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“…In several scenarios, such as the Google TV ad auction [25] and the the FCC spectrum auctions [10], where auctions have been applied in the recent past, bidders are constrained by the amount of money they can spend. This leads to the study of auctions with budget-constrained bidders, which is the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

Budget constrained auctions with heterogeneous items

Bhattacharya

Goel

Gollapudi

et al. 2010

Proceedings of the Forty-Second ACM Symposium on Theory of Computing

142

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In this paper, we present the first approximation algorithms for the problem of designing revenue optimal Bayesian incentive compatible auctions when there are multiple (heterogeneous) items and when bidders have arbitrary demand and budget constraints (and additive valuations). Our mechanisms are surprisingly simple: We show that a sequential all-pay mechanism is a 4 approximation to the revenue of the optimal ex-interim truthful mechanism with a discrete type space for each bidder, where her valuations for different items can be correlated. We also show that a sequential posted price mechanism is a O(1) approximation to the revenue of the optimal ex-post truthful mechanism when the type space of each bidder is a product distribution that satisfies the standard hazard rate condition. We further show a logarithmic approximation when the hazard rate condition is removed, and complete the picture by showing that achieving a sub-logarithmic approximation, even for regular distributions and one bidder, requires pricing bundles of items. Our results are based on formulating novel LP relaxations for these problems, and developing generic rounding schemes from first principles.

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Section: Introductionmentioning

confidence: 99%

Budget constrained auctions with heterogeneous items

Bhattacharya

Goel

Gollapudi

et al. 2010

Proceedings of the Forty-Second ACM Symposium on Theory of Computing

142

View full text Add to dashboard Cite

show abstract

“…Indeed, observed violations of quasilinearity are frequently cited as reasons for why we do not see multiunit Vickrey auctions used in practice. For example, Ausubel and Milgrom (), Rothkopf (), and Nisan et al () all cite budgets and financing constraints as salient features of real‐world auction settings that inhibit the use of the Vickrey auctions. Che and Gale () note that bidders often face increasing marginal costs of expenditures when they have access to imperfect financial markets.…”

Section: Introductionmentioning

confidence: 99%

Efficient multiunit auctions for normal goods

Baisa

2020

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I study multiunit auction design when bidders have private values, multiunit demands, and non‐quasilinear preferences. Without quasilinearity, the Vickrey auction loses its desired incentive and efficiency properties. I give conditions under which we can design a mechanism that retains the Vickrey auction's desirable incentive and efficiency properties: (1) individual rationality, (2) dominant strategy incentive compatibility, and (3) Pareto efficiency. I show that there is a mechanism that retains the desired properties of the Vickrey auction if there are two bidders who have single‐dimensional types. I also present an impossibility theorem that shows that there is no mechanism that satisfies Vickrey's desired properties and weak budget balance when bidders have multidimensional types.

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“…In [7], the authors used the following applications for the sharp multi-demand model. In TV (or radio) advertising [23], advertisers may request different lengths of advertising slots for their ads programs. In banner (or newspaper) advertising, advertisers may request different sizes or areas for their displayed ads, which may be decomposed into a number of base units.…”

Section: Introductionmentioning

confidence: 99%

Approximating the Revenue Maximization Problem with Sharp Demands

Bilò

Flammini

Monaco

2014

Algorithm Theory – SWAT 2014

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Abstract. We consider the revenue maximization problem with sharp multi-demand, in which m indivisible items have to be sold to n potential buyers. Each buyer i is interested in getting exactly di items, and each item j gives a benefit vij to buyer i. We distinguish between unrelated and related valuations. In the former case, the benefit vij is completely arbitrary, while, in the latter, each item j has a quality qj , each buyer i has a value vi and the benefit vij is defined as the product viqj . The problem asks to determine a price for each item and an allocation of bundles of items to buyers with the aim of maximizing the total revenue, that is, the sum of the prices of all the sold items. The allocation must be envy-free, that is, each buyer must be happy with her assigned bundle and cannot improve her utility. We first prove that, for related valuations, the problem cannot be approximated to a factor O(m 1−ǫ ), for any ǫ > 0, unless P = NP and that such result is asymptotically tight. In fact we provide a simple m-approximation algorithm even for unrelated valuations. We then focus on an interesting subclass of "proper" instances, that do not contain buyers a priori known not being able to receive any item. For such instances, we design an interesting 2-approximation algorithm and show that no (2 − ǫ)-approximation is possible for any 0 < ǫ ≤ 1, unless P = NP. We observe that it is possible to efficiently check if an instance is proper, and if discarding useless buyers is allowed, an instance can be made proper in polynomial time, without worsening the value of its optimal solution.

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Google’s Auction for TV Ads

Cited by 32 publications

References 10 publications

Budget constrained auctions with heterogeneous items

Budget constrained auctions with heterogeneous items

Efficient multiunit auctions for normal goods

Approximating the Revenue Maximization Problem with Sharp Demands

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