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... In terms of advertisement slot pricing, cable TV typically uses projected or actual viewership statistics to determine how much to charge advertisers; pricing is based on the number of viewers of said commercial (methods such as Nielsen ratings use statistical sampling to estimate viewership statistics, while methods such as Google TV  use real-time viewership statistics.) An advertisement allocation and charging system, such as that used by Google TV, uses an auction based on prior viewership statistics to assign advertisements to different ad slots and subsequently charges advertisers based on the actual number of viewers of their respective advertisement slot. ...
... We also present a P-time algorithm, which can be run in real-time, to determine the revenue maximizing assignment of the advertisers to the set of advertising slots. Models such as  charge the advertisers ex-post, i.e. the price that the advertisers pay depends not only on their budgets, but also on the actual number of viewers of their commercial. Our model does not require the per commercial feedback of the actual number of viewers in order to determine prices. ...
... In terms of advertisement slot allocation, the AO forwards the viewership statistics / extrapolated expected viewership and current price per advertising slot to the SP and the ADs via data-paths F and A, respectively. In response, each advertiser reports their demand curve for each advertisement, TV program preferences, and desired advertisement length to the AO via datapath B (current TV industry standards use an integer multiple of 15 seconds for advertisement lengths .) The AO then performs an auction to determine the revenue maximizing allocation of advertisements. ...
As the speed of cellular networks has steadily increased so has user consumption of video content. As such, mobile TV deployments over cellular networks have become increasing attractive. Typically mobile TV deployment schemes utilize a subscription-based model. In this model, the advertisements are ``baked'' into the TV program, i.e., the service provider's role is typically limited to that of providing a content distribution network. As such, the service provider's revenue is limited per the contracts negotiated with the content providers. In this paper, we present the design and game theoretic analysis of a non-subscription ad-supported mobile TV system. In our system, we show how to incentivize viewership statistic collection via fair allocation of resources among the TV channels, on a per TV show basis. In addition, we present a real-time auction which enables service providers the ability to directly schedule advertisements/earn revenue based on an accurate set of viewership statistics.
... In several scenarios, such as the Google TV ad auction  and the the FCC spectrum auctions , 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. ...
... An alternative line of work deals with the adversarial setting, where no distributional assumption is made on the bidders' private valuations. In this setting, the budget constrained auction problem is notorious mainly because standard auction concepts such as VCG, efficiency, and competitive equilibria do not directly carry over . Most previous results deal with the case of multiple units of a homogeneous good. ...
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 can have arbitrary demand and budget constraints. 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 discrete correlated type space for each bidder. 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. We believe this approach will be useful in other Bayesian mechanism design contexts. Comment: Final version accepted to STOC '10. Incorporates significant reviewer comments
... For instance, recent work [21,24] shows the impossibility of designing auctions that are Pareto-optimal in terms of welfare even in multi-unit settings. Furthermore, Nisan  show that competitive equilibria need not exist even when agents have additive valuations for items. The lone exceptions are positive results for simple multi-unit settings [21,24,7,1] with unlimited supply of the item. ...
We show that the multiplicative weight update method provides a simple recipe
for designing and analyzing optimal Bayesian Incentive Compatible (BIC)
auctions, and reduces the time complexity of the problem to polynomial in
parameters that depend on single agent instead of on the joint type space. We
use this framework to design the first computationally efficient optimal
auctions that satisfy ex-post Individual Rationality in the presence of
constraints such as (hard, private) budgets and envy-freeness. Our techniques
also yield first optimal auctions when buyers' and seller's utility functions
are non-linear, that includes scenarios such as (a) auctions with "quitting
rights", (b) cost to borrow money beyond budget, (c) buyers' and seller's risk
aversion. We also provide an optimal budget feasible procurement mechanism when
the auctioneer has linear utility.
... This trend started in the sponsored search advertisements (such as, Google's AdWords, Yahoo!'s Search Marketing and Microsoft's AdCenter), and expended to the display advertisement (such as, Double click Ad Exchange ). This trend has even propagated to classical advertisement media, such as TV . ...
Our main goal is to abstract existing repeated sponsored search ad auction mechanisms which incorporate budgets, and study their equilibrium and dynamics. Our abstraction has multiple agents bidding repeatedly for multiple identical items (such as impressions in an ad auction). The agents are budget limited and have a value per item. We abstract this repeated interaction as a one-shot game, which we call budget auction, where agents submit a bid and a budget, and then items are sold by a sequential second price auction. Once an agent exhausts its budget it does not participate in the proceeding auctions.
Our main result shows that if agents bid conservatively (never bid above their value) then there always exists a pure Nash equilibrium. We also study simple dynamics of repeated budget auctions, showing their convergence to a Nash equilibrium for two agents and for multiple agents with identical budgets.
One of the goals of 3rd Generation Partnership Project (3GPP) is to deploy Device to Device (D2D) Proximity Services (ProSe) in both commercial and public safety settings. Furthermore, as the mobile market is nearing saturation, Service Providers (SPs) are searching for additional sources of revenue. Commercial D2D can make use of an SP's underutilized spectrum, yet there are few feasible economic models for deploying commercial D2D services. In this paper we present a feasible economic model for deploying a localized advertisement coalition-based D2D service, which exploits spectrum leasing and reuse. D2D Ad stations, i.e. User Equipments (UEs), interested in providing the D2D service form a coalition in order to share said spectrum. We use game theory to design an incentive compatible auction. We show that our auction approximates the profit performance characteristics of the Vickrey Clarke Groves (VCG) auction, yet runs in P-time. Finally, we show that with proper planning, the D2D Ad stations can be deployed in a manner such that each D2D Ad station can fully exploit the spectrum in the sub-sector formed by the desired cell size. Our framework enables advertisers to indirectly fund the D2D Advertisement service, while also permitting the SP the ability to profit from leasing their under-utilized spectrum to commercial D2D services.
The notion of a "market" has undergone a paradigm shift with the Internet - totally new and highly successful markets have been defined and launched by Internet companies, which already form an important part of today's economy and are projected to grow considerably in the future. Another major change is the availability of massive computational power for running these markets in a centralized or distributed manner. In view of these new realities, the study of market equilibria, an important, though essentially non-algorithmic, theory within mathematical economics, needs to be revived and rejuvenated via an inherently algorithmic approach. Such a theory should not only address traditional market models but also define new models for some of the new markets. We present a new, natural class of utility functions which allow buyers to explicitly provide information on their relative preferences as a function of the amount of money spent on each good. These utility functions offer considerable expressivity, especially in Google's Adwords market. In addition, they lend themselves to efficient computation, while still possessing some of the nice properties of traditional models.
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