The Price of Prior Dependence in Auctions

Tang, Pingzhong; Zeng, Yulong

doi:10.1145/3219166.3219183

Cited by 16 publications

(38 citation statements)

References 18 publications

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“…Due to space limit, here we only briefly discuss the most related works while refer readers to Appendix A.1 for more detailed discussions and comparisons. Closely related to ours is a recent study by Tang and Zeng [36]. They study the bidders' problem of committing to "imitate" a fake value distribution in auctions and acting consistently as if the bidder's value were from the fake distribution.…”

Section: Additional Related Workmentioning

confidence: 74%

“…This is similar in spirit to our buyer's commitment to an imitative value function. However, there is significant difference betweens our setting and that of [36], which leads to very different conclusions as well. Specifically, the seller in [36] auctions a single indivisible item with no production costs whereas our seller sells multiple divisible items with production costs.…”

Section: Additional Related Workmentioning

confidence: 75%

“…However, there is significant difference betweens our setting and that of [36], which leads to very different conclusions as well. Specifically, the seller in [36] auctions a single indivisible item with no production costs whereas our seller sells multiple divisible items with production costs. Another recent work [28] also studies buyer's strategic manipulation against seller's pricing algorithms, but in a single-item multi-buyer situation.…”

Section: Additional Related Workmentioning

confidence: 75%

“…• The buyer with true value function v(x) (unknown to seller) commits to react optimally according to an imitative value function u(x) ∈ C. We remark that such commitment to a fake value function is not uncommon in the literature; similar assumptions have been adopted in many recent works in, e.g., auctions [36], general Stackelberg games [19; 10] and security games [18; 30; 29]. The buyer's ability of making such a commitment fundamentally comes from the fact that the seller has no prior knowledge about the buyer's true value function v(x), i.e., has to "price in the dark".…”

Section: Preliminariesmentioning

confidence: 94%

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The Limits of Optimal Pricing in the Dark

Dawkins¹,

Han²,

Xu³

2021

Preprint

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A ubiquitous learning problem in today's digital market is, during repeated interactions between a seller and a buyer, how a seller can gradually learn optimal pricing decisions based on the buyer's past purchase responses. A fundamental challenge of learning in such a strategic setup is that the buyer will naturally have incentives to manipulate his responses in order to induce more favorable learning outcomes for him. To understand the limits of the seller's learning when facing such a strategic and possibly manipulative buyer, we study a natural yet powerful buyer manipulation strategy. That is, before the pricing game starts, the buyer simply commits to "imitate" a different value function by pretending to always react optimally according to this imitative value function. We fully characterize the optimal imitative value function that the buyer should imitate as well as the resultant seller revenue and buyer surplus under this optimal buyer manipulation. Our characterizations reveal many useful insights about what happens at equilibrium. For example, a seller with concave production cost will obtain essentially 0 revenue at equilibrium whereas the revenue for a seller with convex production cost is the Bregman divergence of her cost function between no production and certain production. Finally, and importantly, we show that a more powerful class of pricing schemes does not necessarily increase, in fact, may be harmful to, the seller's revenue. Our results not only lead to an effective prescriptive way for buyers to manipulate learning algorithms but also shed lights on the limits of what a seller can really achieve when pricing in the dark.

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Section: Additional Related Workmentioning

confidence: 74%

Section: Additional Related Workmentioning

confidence: 75%

Section: Additional Related Workmentioning

confidence: 75%

Section: Preliminariesmentioning

confidence: 94%

See 2 more Smart Citations

The Limits of Optimal Pricing in the Dark

Dawkins¹,

Han²,

Xu³

2021

Preprint

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“…Nedelec et al introduce a technique for the buyer to mitigate the loss of profit due to the seller's reserve price optimisation strategy [24,25]. Their presentation use functional analysis tool, while Tang et al's is based on quantiles [26]. Our work belongs to this category.…”

Section: Bidding In Display Advertising Auction Marketsmentioning

confidence: 99%

How to bid in unified second-price auctions when requests are duplicated

Heymann

2020

Operations Research Letters

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In display advertising auctions, a unique display opportunity may trigger many bid requests being sent to the same buyer. Bid request duplication is an issue: programmatic bidding agents might bid against themselves. In a simplified setting of unified second-price auctions, the optimal solution for the bidder is to randomize the bid, which is quite unusual. Our results motivate the recent switch to a unified first-price auction by showing that a unified second-price auction could have been detrimental to all participants.

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