Boosted Second-price Auctions for Heterogeneous Bidders

Golrezaei, Negin; Lin, Max S.; Mirrokni, Vahab; Nazerzadeh, Hamid

doi:10.2139/ssrn.3016465

Cited by 20 publications

(34 citation statements)

References 3 publications

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“…With respect to the homogeneity assumption, we point out that there exists empirical evidence that buyers are indeed heterogeneous (Guimaraes and Sheedy 2011, Johnson and Myatt 2003, Golrezaei et al 2017a). It is not surprising that the heterogeneity in the markets makes the design of selling mechanisms more difficult.…”

Section: Related Workmentioning

confidence: 85%

Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions

2018

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Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions. Buyers' valuations for an item depend on the context that describes the item. However, the seller is not aware of the relationship between the context and buyers' valuations, i.e., buyers' preferences. The seller's goal is to design a learning policy to set reserve prices via observing the past sales data, and her objective is to minimize her regret for revenue, where the regret is computed against a clairvoyant policy that knows buyers' heterogeneous preferences. Given the seller's goal, utility-maximizing buyers have the incentive to bid untruthfully in order to manipulate the seller's learning policy. We propose learning policies that are robust to such strategic behavior. These policies use the outcomes of the auctions, rather than the submitted bids, to estimate the preferences while controlling the long-term effect of the outcome of each auction on the future reserve prices. When the market noise distribution is known to the seller, we propose a policy called Contextual Robust Pricing (CORP) that achieves a T-period regret of O(d log(T d) log(T )), where d is the dimension of the contextual information.When the market noise distribution is unknown to the seller, we propose two policies whose regrets are sublinear in T .

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Section: Related Workmentioning

confidence: 85%

Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions

2018

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“…In an online auction setting, Balseiro et al (2015) introduce the notion of a fluid mean-field equilibrium and illustrate its practical appeal in setting reserve prices. In addition, Golrezaei et al (2017) show empirically that the platform's revenues increase by 3% when it boosts bids in a customized fashion based on bidders' past behaviors. Our work contributes to this literature by empirically demonstrating the impact of market thickness on bidders' participation and bidding decisions on a platform specializing in B2B…”

Section: Related Literaturementioning

confidence: 99%

Managing Market Thickness in Online Business-to-Business Markets

et al. 2020

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We explore marketplace design in the context of a business-to-business platform specializing in liquidation auctions. Even when the platform’s aggregate levels of supply and demand remain fixed, we establish that the platform’s ability to use its design levers to manage the availability of supply over time yields significant value. We study two such levers, each using the platform’s availability of supply as a means to incentivize participation from buyers who decide strategically when/how often to participate. First, the platform’s listing policy sets the ending times of incoming auctions (hence, the frequency of market clearing). Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% mainly by inducing a higher level of bidder participation. The second lever is a recommendation system that can be used to reveal information about real-time market thickness to potential bidders. The optimization of these levers highlights a novel trade-off. Namely, when the platform consolidates auctions’ ending times, more bidders may participate in the marketplace (demand-side competition); but ultimately auctions for substitutable goods cannibalize one another (supply-side competition). To optimize these design decisions, we estimate a structural model that endogenizes bidders’ dynamic behavior, that is, their decisions on whether/how often to participate in the marketplace and how much to bid. We find that appropriately designing a recommendation system yields an additional revenue increase (on top of the benefits obtained by optimizing the platform’s listing policy) by reducing supply-side cannibalization and altering the composition of participating bidders. This paper was accepted by Vishal Gaur, operations management.

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“…Our paper focuses on a set that we call performance buyers, which are buyers that focus on short-term financial performance above long-term outcomes. This idea is borrowed from [13].…”

Section: Related Literaturementioning

confidence: 99%

Auction Design for ROI-Constrained Buyers

Golrezaei

Lobel

Leme

2021

Proceedings of the Web Conference 2021

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We combine theory and empirics to (i) show that some buyers in online advertising markets are financially constrained and (ii) demonstrate how to design auctions that take into account such financial constraints. We use data from a field experiment where reserve prices were randomized on Google's advertising exchange (AdX). We find that, contrary to the predictions of classical auction theory, a significant set of buyers lowers their bids when reserve prices go up. We show that this behavior can be explained if we assume buyers have constraints on their minimum return on investment (ROI). We proceed to design auctions for ROI-constrained buyers. We show that optimal auctions for symmetric ROI-constrained buyers are either second-price auctions with reduced reserve prices or subsidized second-price auctions. For asymmetric buyers, the optimal auction involves a modification of virtual values. Going back to the data, we show that using ROI-aware optimal auctions can lead to large revenue gains and large welfare gains for buyers.

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Boosted Second-price Auctions for Heterogeneous Bidders

Cited by 20 publications

References 3 publications

Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions

Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions

Managing Market Thickness in Online Business-to-Business Markets

Auction Design for ROI-Constrained Buyers

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