Generalized Second Price Research Articles

Cost-Effective Acquisition of First-Party Data for Business Analytics

Customer data acquisition is an important task in data-driven business analytics. Recently, there has been a growing interest in the effective use of an organization’s internal customer data, also known as first-party data. This work studies the acquisition of new data for business analytics based on first-party data resource. We address issues related to both acquisition cost and data quality. To reduce acquisition cost, we consider using auction-based methods, such as the generalized second price (GSP) auction, for acquiring data with differential prices for different customers. We find that the GSP-based data acquisition method incurs a lower cost and/or achieves a higher response rate than fixed price methods. To maximize data quality, we propose novel optimization models for different data acquisition methods and data quality measures. The proposed models maximize the quality of the acquired data while satisfying budget constraints. We derive and discuss the solutions to the optimization models analytically and provide managerial insights from the solutions. The proposed approach is effective in increasing customer responses, reducing selection bias, and enabling more accurate estimation and prediction for business analytics. The results of the experimental evaluation demonstrate the advantage of the proposed approach over existing data acquisition methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0037 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0037 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

A Cost-Aware Utility-Maximizing Bidding Strategy for Auction-Based Federated Learning.

Auction-based federated learning (AFL) has emerged as an efficient and fair approach to incentivize data owners (DOs) to contribute to federated model training, garnering extensive interest. However, the important problem of helping data consumers (DCs) bid for DOs in competitive AFL settings remains open. Existing work simply treats that the actual cost paid by a winning DC (i.e., the bid cost) is equal to the bid price offered by that DC itself. However, this assumption is inconsistent with the widely adopted generalized second-price (GSP) auction mechanism used in AFL, including in these existing works. Under a GSP auction, the winning DC does not pay its own proposed bid price. Instead, the bid cost for the winner is determined by the second-highest bid price among all participating DCs. To address this limitation, we propose a first-of-its-kind federated cost-aware bidding strategy () to help DCs maximize their utility under GSP auction-based federated learning (FL). It enables DCs to efficiently bid for DOs in competitive AFL markets, maximizing their utility and improving the resulting FL model accuracy. We first formulate the optimal bidding function under the GSP auction setting, and then demonstrate that it depends on utility estimation and market price modeling, which are interrelated. Based on this analysis, jointly optimizes in a novel end-to-end framework, and then executes the proposed return on investment (ROI)-based method to determine the optimal bid price for each piece of the data resource. Through extensive experiments on six commonly adopted benchmark datasets, we show that outperforms eight state-of-the-art methods, beating the best baseline by 4.39%, 4.56%, 1.33%, and 5.43% on average in terms of the total amount of data obtained, number of data samples per unit cost, total utility, and FL model accuracy, respectively.

Nontruthful Position Auctions Are More Robust to Misspecification

We consider the classical model of sponsored search due to Edelman et al. and Varian and examine how robust standard position auctions are to a misspecification of the position-dependent quality factors used by this model. We show that under both complete and incomplete information a nontruthful position auction admits an efficient equilibrium for a strictly broader range of parameter values than the Vickrey-Clarke-Groves (VCG) mechanism, which would be truthful if the parameters were specified correctly. Our result for complete information concerns the generalized second-price (GSP) mechanism and is driven by a detailed understanding of the Nash equilibrium polytopes of the VCG mechanism and the GSP mechanism. Our result for incomplete information concerns the generalized first-price (GFP) mechanism and uses a surprising connection between the unique candidate equilibrium bidding functions of the VCG mechanism and the GFP mechanism. Funding: F. Fischer was supported by the Engineering and Physical Sciences Research Council [Grant EP/T015187/1] and Einstein Stiftung Berlin.

Devising a trading mechanism with a joint price adjustment for local electricity markets using blockchain. Insights for policy makers

The new potential of Distributed Energy Resources (DER), residential consumers, buildings, and prosumers in terms of controllable devices, self-generation, and storage makes them more active in the market encouraged by lower electricity prices. In addition, integrating a higher volume of volatile Renewable Energy Sources (RES) that unstress the public grid by local trading interactions is a desirable target of Local Electricity Markets (LEM). In this paper, we propose a blockchain trading mechanism to simulate the electricity transactions for 11 modern smart houses with more than 300 appliances, 8 roof- or faced-PV systems, and smart-metered 15-min readings that form a small-size community. The electricity generated at the community level lowers the electricity bills and brings benefits for prosumers (sellers) and consumers (buyers). Several trading mechanisms for LEM transactions including auctions such as Uniform Price (UP), Pay-As-Bid (PAB), Generalized Second-Price (GSP), Vickrey-Clark-Groves (VCG) methods are implemented to evaluate the benefits and show their efficiency. After the market is initially cleared, an adjustment coefficient of the price is proposed for both sides (seller and buyer) to enlarge the trading potential at the community level using blockchain technology. It proves to bring excellent results to the LEM participants and enhance trading with outstanding benefits.

Mixed Mechanisms for Auctioning Ranked Items

This paper deals with the problem of designing and choosing auctioning mechanisms for multiple commonly ranked objects as, for instance, keyword auctions in search engines on Internet. We shall adopt the point of view of the auctioneer who has to select the auction mechanism to be implemented not only considering its expected revenue, but also its associated risk. In order to do this, we consider a wide parametric family of auction mechanisms which contains the generalizations of discriminatory-price auction, uniform-price auction and Vickrey auction. For completeness, we also analyze the Generalized Second Price (GSP) auction which is not in the family. The main results are: (1) all members of the family satisfy the four basic properties of fairness, no over-payment, optimality and efficiency, (2) the Bayesian Nash equilibrium and the corresponding value at risk for the auctioneer are obtained for the considered auctions, (3) the GSP and all auctions in the family provide the same expected revenue, (4) there are new interesting auction mechanisms in the family which have a lower value at risk than the GSP and the classical auctions. Therefore, a window opens to apply new auction mechanisms that can reduce the risk to be assumed by auctioneers.

Fast Core Pricing for Rich Advertising Auctions

Standard ad auction formats do not immediately extend to settings where multiple size configurations and layouts are available to advertisers. In these settings, the sale of web advertising space increasingly resembles a combinatorial auction with complementarities, where truthful auctions such as the Vickrey-Clarke-Groves (VCG) can yield unacceptably low revenue. We therefore study core selecting auctions, which boost revenue by setting payments so that no group of agents, including the auctioneer, can jointly improve their utilities by switching to a different outcome. Our main result is a combinatorial algorithm that finds an approximate bidder optimal core point with almost linear number of calls to the welfare maximization oracle. Our algorithm is faster than previously-proposed heuristics in the literature and has theoretical guarantees. We conclude that core pricing is implementable even for very time sensitive practical use cases such as realtime auctions for online advertising and can yield more revenue. We justify this claim experimentally using the Microsoft Bing Ad Auction data, through which we show our core pricing algorithm generates almost 26% more revenue than VCG on average, about 9% more revenue than other core pricing rules known in the literature, and almost matches the revenue of the standard Generalized Second Price (GSP) auction.

A Novel GSP Auction Mechanism for Dynamic Confirmation Games on Bitcoin Transactions

Bitcoin is gaining ground in recent years. In the Bitcoin system, miners provide computing power to confirm transactions in pursuit of transaction fees, while users compete by bidding transaction fees for faster confirmation. This process is in essence analogous to online ad auctions, where advertisers bid for more prominent ad slots. Therefore, inspired by ad auction research, we propose to apply the Generalized Second Price (GSP) auction mechanism in the dynamic confirmation game on Bitcoin transactions. Our model is targeted to deal with the problems caused by instability and low efficiency in the currently-adopted Generalized First Price (GFP) auction model in Bitcoin confirmation games. Besides, we use the “rank-by-cost” rule to replace the “rank-by-fee” rule, where each transaction’s cost is calculated by the user-submitted fee and the waiting time. Aiming to probe users’ equilibrium strategy, we first discuss the GSP game with complete information under synchronous submissions, and show that it has the Locally Envy-Free equilibrium. Then, we study the GSP game with incomplete information under asynchronous submissions, and define two types of strategies, i.e., the Farsighted Balanced (FB) strategy and the Instant Balanced (IB) strategy. The FB strategy is in line with users’ practical needs of determining fees so as to maximize the long-term payoffs; however it cannot generate a stable equilibrium. Alternatively, the IB strategy focuses on the instant payoff maximization, and if all users follow the IB strategy, their equilibrium fees can finally converge to a stable profile. Finally, we design computational experiments to validate our theoretical models and analysis. Our research findings indicate that this novel GSP mechanism is superior to the currently adopted GFP mechanism. Besides, the convergence of the GSP game under the IB strategy has also been illustrated by the computational experiments.

Learning and pricing models for repeated generalized second-price auction in search advertising

In search advertising how much an advertiser is willing to pay for a click or tap on his search ad is private information, which hampers an ad seller’s ability to set the best reserve price to increase the revenue for the generalized second-price (GSP) auctions used to allocate ad slots. We present a series of learning and pricing models for repeated GSP auctions selling multiple heterogeneous items. This paper contributes to the literature in dynamic pricing with learning and complements the existing off-line studies on impact of the reserve price in the multi-billion dollar online advertising business. With few restrictions on the distribution function of the unknown parameter, algorithms are developed to estimate the empirical distribution function and determine the best reserve price to reduce the revenue loss (regret) over time. When bidders bid in the locally envy-free equilibrium, we present an algorithm that has the best attainable regret upper bound. When bidders do not bid in the locally envy-free equilibrium, we propose a GSP auction with position-specific reserve prices and develop an algorithm with the same regret bound to mitigate the risk of strategic bidding. With a high volatility involved, learning becomes more active while earning is more effective. When bidders coordinate bidding, the properly selected starting reserve prices can substantially reduce the revenue loss from possible collusive bidding behaviors.

A novel GSP auction mechanism for ranking Bitcoin transactions in blockchain mining

Bitcoin is gaining ground in recent years. In the Bitcoin system, miners provide computing power to confirm transactions and mine blocks in pursuit of transaction fees, while users compete by bidding transaction fees for faster confirmation. This process is in essence analogous to online ad auctions, where advertisers bid for more prominent ad slots. Therefore, inspired by the Generalized Second Price (GSP) mechanism dominantly used in online ad auctions, we propose to adopt the GSP auction model in the Bitcoin transaction confirmation game. Also, we use weighted fees as the new ranking basis, which can be calculated by user-submitted fees, transaction size, quality scores and virtual fees accumulated from the waiting time. We show that the formulated static GSP transaction fee auction with complete information has a unique Pure Strategy Nash Equilibrium. Then, we discuss the impacts of quality scores and virtual fees on users' equilibrium fee decisions and payoffs. Finally, computational experiments are designed to validate our theoretical models and analysis. Our research findings indicate that this novel GSP mechanism is superior to the currently adopted GFP mechanism, and can help users save fees. Besides, quality scores and virtual fees are also proven to be effective on reducing users' paid fees. Moreover, the design of virtual fees allows all transactions to be processed more efficiently in a uniform pipeline, and the interests of transactions with and without associated fees are taken into consideration.

QoS Maintenance in Cognitive Radio Networks with Priority-Supporting Novel Channel Allocation Method

Satisfying the Quality of Service (QoS) is often a challenge in cognitive radio networks, because they depend on opportunistic channel accessing. In this context, appropriate pricing of vacant channels that is linked to the preference in their allocation, is found to be useful. However, ambiguity on the possible price at which the channel would be allotted is still a concern. In this work, an auction mechanism in which maximum value of the bid is predefined is proposed. With this, users quote their bid values as per their needs of getting the channels, up to the predefined maximum allowed bid price. However, final price of allocation is decided based on the sum total demand from all the users and the availability of vacant channels. Performance of the system is found in terms of blocking probabilities of secondary users and revenues to primary users. The proposed system is found to yield similar quantum of revenues as that of the Generalized Second Price (GSP) auction, while offering much lesser blocking probabilities to high-priority users to satisfy their QoS requirements.

An experimental study of the generalized second price auction

We experimentally investigate the Generalized Second Price (GSP) auction used to sell advertising positions in online search engines. Two contrasting click through rates (CTRs) are studied, under both static complete and dynamic incomplete information settings. Subjects consistently bid above the Vikrey–Clarke–Grove's (VCG) like equilibrium favored in the theoretical literature. However, bidding, at least qualitatively, satisfies the contrasting outcomes predicted under the two CTRs. For both CTRs, outcomes under the static complete information environment are similar to those in later rounds of the dynamic incomplete information environment. This supports the theoretical literature that uses the static complete information model as an approximation to the dynamic incomplete information under which advertising positions are allocated in field settings.

Exclusive Placement in Online Advertising

A recent development in online advertising has been the ability of advertisers to have their ads displayed exclusively on (a part of) a web page. We study this phenomenon in the context of both sponsored search advertising and display advertising. Ads are sold through auctions, and when exclusivity is allowed, the seller accepts two bids from advertisers, where one bid is for the standard display format in which multiple advertisers are displayed, and the other bid is for being shown exclusively (therefore they are called two-dimensional, or 2D, auctions). We identify two opposing forces at play in an auction that provides the exclusive placement option—allowing more flexible expression of preferences through bidding for exclusivity increases competition among advertisers, leading to higher bids, which increases the seller’s revenue (between-advertiser competition effect), but it also gives advertisers the incentive to shade their bids for their nonpreferred outcomes, which decreases the seller’s revenue (within-advertiser competition effect). Depending on which effect is stronger, the revenue may increase or decrease. We find that the 2D generalized second price (GSP2D) auction, which is an extension of the widely used generalized second price (GSP) auction and on which currently used auctions for exclusive placement are based, may lead to higher or lower revenue under different parametric conditions. Paradoxically, the revenue from allowing exclusive placement decreases as bidders have higher valuations for exclusive placement. We verify several key implications from our analysis of GSP2D using data from Bing for over 100,000 auctions. As a possible solution (applicable to both sponsored search and display advertising), we show that using VCG2D, which is the adaptation of the Vickrey–Clarke–Groves (VCG) auction for the 2D setting, guarantees weakly higher revenue when exclusive display is allowed. This is because it induces truthful bidding, which alleviates the problem of bid shading due to the within-advertiser competition effect. The online appendix is available at https://doi.org/10.1287/mksc.2018.1098 .

Generalized Second Price Auctions over a Network

We consider the problem of how to apply a generalized second price (GSP) auction to a buyer–seller network. GSP auctions are often used to sell online ads where buyers care about the position or placement of the ad. GSP auctions can also be applied to wireless data transmissions with congestion where buyers care about the speed of data transmission; however, such an auction would take place over a network as a buyer could only purchase from a seller (or cell tower) that he was linked to (or was close to). Two GSP auctions over a network are considered: separate GSP auctions, and integrated GSP auctions with pauses. The efficiency of these auctions is examined with efficiency favoring the integrated auction with pauses.

Hybrid mechanisms for Vickrey–Clarke–Groves and generalized second-price bids

This paper analyzes mechanisms for selling advertising opportunities for several different positions on a page that would enable some advertisers to bid using Vickrey–Clarke–Groves (VCG) pricing while other advertisers bid using generalized second-price (GSP) pricing. I focus on a setting in which the number of ads displayed may vary with the advertisers’ bids and showing fewer ads enables the remaining ads to obtain more clicks. I illustrate the types of mechanisms one can construct that would ensure that VCG bidders have an incentive to bid truthfully, GSP bidders cannot obtain the same number of clicks at a lower price by varying their bid, and if all bidders bid using VCG (GSP) pricing, then the outcome is the same as it would be under the VCG (GSP) mechanism.

Auctions for online ad space among advertisers sensitive to both views and clicks

Advertisement in dedicated webpage spaces or in search engines sponsored slots is usually sold using auctions, with a payment rule that is either per view or per click. But advertisers can be both sensitive to being viewed (brand awareness effect) and being clicked (conversion into sales). In this paper, we generalize the auction mechanism by including both pricing components: the displayed advertisers are charged when their ad is displayed, and pay an additional price if the ad is clicked. Applying the results for Vickrey-Clarke-Groves (VCG) auctions, we show how to compute payments to ensure incentive compatibility from advertisers as well as maximize the total value of the advertisement slot(s). We provide tight upper bounds for the loss of efficiency due to applying only pay-per-click (or pay-per-view) pricing instead of our scheme. Those bounds depend on the joint distribution of advertisement visibility and population likelihood to click on ads, and can help identify situations where our mechanism yields significant improvements. We also describe how the commonly used generalized second price (GSP) auction can be extended to this context.

Adopting the Cascade Model in Ad Auctions: Efficiency Bounds and Truthful Algorithmic Mechanisms

Sponsored Search Auctions (SSAs) are one of the most successful applications of microeconomic mechanisms, with a revenue of about $72 billion in the US alone in 2016. However, the problem of designing the best economic mechanism for sponsored search auctions is far from being solved, and, given the amount at stake, it is no surprise that it has received growing attention over the past few years. The most common auction mechanism for SSAs is the Generalized Second Price (GSP). However, the GSP is known not to be truthful: the agents participating in the auction might have an incentive to report false values, generating economic inefficiency and suboptimal revenues in turn. Superior, efficient truthful mechanisms, such as the Vickrey-Clarke-Groves (VCG) auction, are well known in the literature. However, while the VCG auction is currently adopted for the strictly related scenario of contextual advertising, e.g., by Google and Facebook, companies are reluctant to extend it to SSAs, fearing prohibitive switching costs. Other than truthfulness, two issues are of paramount importance in designing effective SSAs. First, the choice of the user model; not only does an accurate user model better target ads to users, it also is a critical factor in reducing the inefficiency of the mechanism. Often an antagonist to this, the second issue is the running time of the mechanism, given the performance pressure these mechanisms undertake in real-world applications. In our work, we argue in favor of adopting the VCG mechanism based on the cascade model with ad/position externalities (APDC-VCG). Our study includes both the derivation of inefficiency bounds and the design and the experimental evaluation of exact and approximate algorithms.

Position Ranking and Auctions for Online Marketplaces

Online e-commerce platforms such as Amazon and Taobao connect thousands of sellers and consumers every day. In this work, we study how such platforms should rank products displayed to consumers, and utilize the top and most salient slots. We present a model that considers consumers' search costs and the externalities sellers impose on each other. This model allows us to study a multi-objective optimization, whose objective includes consumer and seller surplus, as well as the sales revenue, and derive the optimal ranking decision. In addition, we propose a so-called surplus-ordered ranking (SOR) mechanism for selling some of the top slots. This mechanism is motivated in part by Amazon's sponsored search program. We show that our mechanism is near-optimal, performing significantly better than those that do not incentivize the sellers to reveal their private information. In addition, when the platform sells all slots, we show that our mechanism can be implemented as a Nash equilibrium in a modified generalized second price (GSP) auction.

Matching Value and Market Design in Online Advertising Networks: An Empirical Analysis

Advertising networks have recently played an increasingly important role in the online advertising market. Critical to the success of an advertising network are two mechanisms: an allocation mechanism that efficiently matches advertisers with publishers and a pricing scheme that maximally extracts surplus from the matches. In this paper, we quantify the value and investigate the determinants of a successful advertiser-publisher match, using data from Taobao’s advertising network. A counterfactual experiment reveals that the platform’s profit under a decentralized allocation mechanism is close to the profit level when the platform centrally assigns the matching under perfect platform knowledge of matching values. In another counterfactual experiment, we explore the effect of platform technology and revenue model on the strategic choice of the pricing schemes of list price versus generalized second price (GSP) auction pricing. We find that platforms that profit from the advertiser side may have less incentive to adopt GSP auction than platforms that profit from the publisher side.

On the Stability of Generalized Second Price Auctions with Budgets

The Generalized Second Price (GSP) auction used typically to model sponsored search auctions does not include the notion of budget constraints, which is present in practice. Motivated by this, we introduce the different variants of GSP auctions that take budgets into account in natural ways. We examine their stability by focusing on the existence of Nash equilibria and envy-free assignments. We highlight the differences between these mechanisms and find that only some of them exhibit both notions of stability. This shows the importance of carefully picking the right mechanism to ensure stable outcomes in the presence of budgets.

Keyword Search Advertising and First-Page Bid Estimates: A Strategic Analysis

In using the generalized second-price (GSP) auction to sell advertising slots, a search engine faces several challenges. Advertisers do not truthfully bid their valuations, and the valuations are uncertain. Furthermore, advertisers are budget constrained. In this paper we analyze a stylized model of the first-page bid estimate (FPBE) mechanism first developed by Google and demonstrate its advantages in dealing with these challenges. We show why and when the FPBE mechanism yields higher profits for the search engine compared with the traditional GSP auction and the GSP auction with advertiser-specific minimum bid. In the event that a high-valuation advertiser is budget constrained, the search engine can use the FPBE mechanism to alter the listing order with the intent of keeping the high-valuation advertiser in the auction for a longer time. The resulting increase in the search engine’s profits is not necessarily at the expense of the advertisers because the combined profits of the advertisers and the search engine increase. This paper was accepted by J. Miguel Villas-Boas, marketing.