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1. Contextual Standard Auctions with Budgets: Revenue Equivalence and Efficiency Guarantees

   https://doi.org/10.1287/mnsc.2023.4719  

   Balseiro, Santiago; Kroer, Christian; Kumar, Rachitesh (January 2023, Management Science)

   The internet advertising market is a multibillion dollar industry in which advertisers buy thousands of ad placements every day by repeatedly participating in auctions. An important and ubiquitous feature of these auctions is the presence of campaign budgets, which specify the maximum amount the advertisers are willing to pay over a specified time period. In this paper, we present a new model to study the equilibrium bidding strategies in standard auctions, a large class of auctions that includes first and second price auctions, for advertisers who satisfy budget constraints on average. Our model dispenses with the common yet unrealistic assumption that advertisers’ values are independent and instead assumes a contextual model in which advertisers determine their values using a common feature vector. We show the existence of a natural value pacing–based Bayes–Nash equilibrium under very mild assumptions. Furthermore, we prove a revenue equivalence showing that all standard auctions yield the same revenue even in the presence of budget constraints. Leveraging this equivalence, we prove price of anarchy bounds for liquid welfare and structural properties of pacing-based equilibria that hold for all standard auctions. In recent years, the internet advertising market has adopted first price auctions as the preferred paradigm for selling advertising slots. Our work, thus, takes an important step toward understanding the implications of the shift to first price auctions in internet advertising markets by studying how the choice of the selling mechanism impacts revenues, welfare, and advertisers’ bidding strategies. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4719 . 

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2. Untangling Header Bidding Lore

   https://doi.org/10.1007/978-3-030-44081-7_17  

   Aqeel, Waqar; Bhattacherjee, Debopam; Chandrasekaran, Balakrishnan; Godfrey, P. Brighten; Laughlin, Gregory; Maggs, Bruce; Singla, Ankit (April 2020, Passive and Active Measurement)

   Header bidding (HB) is a relatively new online advertising technology that allows a content publisher to conduct a client-side (i.e., from within the end-user’s browser), real-time auction for selling ad slots on a web page. We developed a new browser extension for Chrome and Firefox to observe this in-browser auction process from the user’s perspective. We use real end-user measurements from 393,400 HB auctions to (a) quantify the ad revenue from HB auctions, (b) estimate latency overheads when integrating with ad exchanges and discuss their implications for ad revenue, and (c) break down the time spent in soliciting bids from ad exchanges into various factors and highlight areas for improvement. For the users in our study, we find that HB increases ad revenue for web sites by 28% compared to that in real-time bidding as reported in a prior work. We also find that the latency overheads in HB can be easily reduced or eliminated and outline a few solutions, and pitch the HB platform as an opportunity for privacy-preserving advertising. 

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3. Riggs: Decentralized Sealed-Bid Auctions

   Tyagi, Nirvan; Arun, Arasu; Freitag, Cody; Wahby, Riad; Bonneau, Joseph; Mazières, David (November 2023, ACM Conference on Computer and Communications Security (CCS))

   Cremers, Cas; Kirda, Engin (Ed.)

   We introduce the first practical protocols for fully decentralized sealed-bid auctions using timed commitments. Timed commitments ensure that the auction is finalized fairly even if all participants drop out after posting bids or if bidders collude to try to learn the bidder’s bid value. Our protocols rely on a novel non-malleable timed commitment scheme which efficiently supports range proofs to establish that bidders have sufficient funds to cover a hidden bid value. This allows us to penalize users who abandon bids for exactly the bid value, while supporting simultaneous bidding in multiple auctions with a shared collateral pool. Our protocols are concretely efficient and we have implemented them in an Ethereum- compatible smart contract which automatically enforces payment and delivery of an auctioned digital asset. 

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4. "Asymmetric Information and Sovereign Debt: Theory Meets Mexican Data."

   Cole, Harold; Neuhann, Daniel; Ordonez, Guillermo (August 2022, Journal of political economy)

   Greg Kaplan (Ed.)

   Using bid-level data from discriminatory auctions for Mexican government bonds, we demonstrate that asymmetric information about default risk is a key friction in sovereign bond markets. We document that large bidders achieve higher bid-acceptance rates than other bidders despite paying no more for executed bids.We then propose a new model of primary markets in which investors may differ in wealth, risk aversion, market power, and information. Only asymmetric information can qualitatively account for our empirical finding, and asymmetric information about rare disasters can quantitatively match bidding and yield moments. Counterfactuals reveal substantial effects of asymmetric information on yields. 

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5. Bayesian Calibrated Click-Through Auctions

   https://doi.org/10.4230/LIPIcs.ICALP.2024.44  

   Chen, Junjie; Li, Minming; Xu, Haifeng; Zuo, Song (January 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Bringmann, Karl; Grohe, Martin; Puppis, Gabriele; Svensson, Ola (Ed.)

   We study information design in click-through auctions, in which the bidders/advertisers bid for winning an opportunity to show their ads but only pay for realized clicks. The payment may or may not happen, and its probability is called the click-through rate (CTR). This auction format is widely used in the industry of online advertising. Bidders have private values, whereas the seller has private information about each bidder’s CTRs. We are interested in the seller’s problem of partially revealing CTR information to maximize revenue. Information design in click-through auctions turns out to be intriguingly different from almost all previous studies in this space since any revealed information about CTRs will never affect bidders' bidding behaviors - they will always bid their true value per click - but only affect the auction’s allocation and payment rule. In some sense, this makes information design effectively a constrained mechanism design problem. Our first result is an FPTAS to compute an approximately optimal mechanism under a constant number of bidders. The design of this algorithm leverages Bayesian bidder values which help to "smooth" the seller’s revenue function and lead to better tractability. The design of this FPTAS is complex and primarily algorithmic. Our second main result pursues the design of "simple" mechanisms that are approximately optimal yet more practical. We primarily focus on the two-bidder situation, which is already notoriously challenging as demonstrated in recent works. When bidders' CTR distribution is symmetric, we develop a simple prior-free signaling scheme, whose construction relies on a parameter termed optimal signal ratio. The constructed scheme provably obtains a good approximation as long as the maximum and minimum of bidders' value density functions do not differ much. 

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