Ex-post IR Dynamic Auctions with Cost-per-Action Payments

Shen, Weiran; Wang, Zihe; Zuo, Song

doi:10.24963/ijcai.2018/70

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…These works assume that each buyer has an underlying bid or value distribution and it does not change over time. There are also a series of works that optimize the revenue in dynamic auctions with the so-called "bank account" mechanisms (Mirrokni et al 2016a;2016b;Shen, Wang, and Zuo 2018). Battaglini (2005) study the Markovian consumer model in a long-term contract setting.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions

Shen

Peng

Liu

et al. 2020

AAAI

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In many social systems in which individuals and organizations interact with each other, there can be no easy laws to govern the rules of the environment, and agents' payoffs are often influenced by other agents' actions. We examine such a social system in the setting of sponsored search auctions and tackle the search engine's dynamic pricing problem by combining the tools from both mechanism design and the AI domain. In this setting, the environment not only changes over time, but also behaves strategically. Over repeated interactions with bidders, the search engine can dynamically change the reserve prices and determine the optimal strategy that maximizes the profit. We first train a buyer behavior model, with a real bidding data set from a major search engine, that predicts bids given information disclosed by the search engine and the bidders' performance data from previous rounds. We then formulate the dynamic pricing problem as an MDP and apply a reinforcement-based algorithm that optimizes reserve prices over time. Experiments demonstrate that our model outperforms static optimization strategies including the ones that are currently in use as well as several other dynamic ones.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions

Shen

Peng

Liu

et al. 2020

AAAI

Self Cite

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“…In particular, when running a sequence of repeated auctions on online advertising platforms, using dynamic auctions optimized across different time periods could potentially bring significant gains both in terms of revenue and social welfare. The power of dynamic mechanisms has been investigated by a number of recent papers [Bergemann and Välimäki, 2002;Parkes and Singh, 2004;Cavallo, 2008;Athey and Segal, 2013;Kakade et al, 2013;Pai and Vohra, 2013;Pavan et al, 2014;Devanur et al, 2015;Balseiro et al, 2016;Chawla et al, 2016;Balseiro et al, 2017a,b;Lobel and Paes Leme, 2017;Shen et al, 2018;Balseiro et al, 2019]. We would also refer to Bergemann and Said [2011] and Bergemann and Välimäki [2017] for comprehensive surveys on the subject.…”

Section: Introductionmentioning

confidence: 99%

Optimal Dynamic Auctions Are Virtual Welfare Maximizers

Mirrokni

Leme

Tang

et al. 2019

AAAI

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We are interested in the setting where a seller sells sequentially arriving items, one per period, via a dynamic auction. At the beginning of each period, each buyer draws a private valuation for the item to be sold in that period and this valuation is independent across buyers and periods. The auction can be dynamic in the sense that the auction at period t can be conditional on the bids in that period and all previous periods, subject to certain appropriately defined incentive compatible and individually rational conditions. Perhaps not surprisingly, the revenue optimal dynamic auctions are computationally hard to find and existing literatures that aim to approximate the optimal auctions are all based on solving complex dynamic programs.It remains largely open on the structural interpretability of the optimal dynamic auctions.In this paper, we show that any optimal dynamic auction is a virtual welfare maximizer subject to some monotone allocation constraints. In particular, the explicit definition of the virtual value function above arises naturally from the primal-dual analysis by relaxing the monotone constraints. We further develop an ironing technique that gets rid of the monotone allocation constraints. Quite different from Myerson's ironing approach, our technique is more technically involved due to the interdependence of the virtual value functions across buyers. We nevertheless show that ironing can be done approximately and efficiently, which in turn leads to a Fully Polynomial Time Approximation Scheme of the optimal dynamic auction. * The authors thank the anonymous reviewers for their helpful comments.

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