Large fractions of online advertisements are sold via repeated second-price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: how can the auctioneer optimize reserve prices by learning from the previous bids while accounting for the long-term incentives and strategic behavior of the bidders? To this end, we consider a seller who repeatedly sells ex ante identical items via a second-price auction. Buyers’ valuations for each item are drawn independently and identically from a distribution F that is unknown to the seller. We find that if the seller attempts to dynamically update a common reserve price based on the bidding history, this creates an incentive for buyers to shade their bids, which can hurt revenue. When there is more than one buyer, incentive compatibility can be restored by using personalized reserve prices, where the personal reserve price for each buyer is set using the historical bids of other buyers. Such a mechanism asymptotically achieves the expected revenue obtained under the static Myerson optimal auction for F. Further, if valuation distributions differ across bidders, the loss relative to the Myerson benchmark is only quadratic in the size of such differences. We extend our results to a contextual setting where the valuations of the buyers depend on observed features of the items. When up-front fees are permitted, we show how the seller can determine such payments based on the bids of others to obtain an approximately incentive-compatible mechanism that extracts nearly all the surplus.
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On Price versus Quality
In this work we propose a model where the value of a buyer for some product (like a slice of pizza)
is a combination of their personal desire for the product (how hungry they are for pizza) and
the quality of the product (how good the pizza is). Sellers in this setting have a two-dimensional
optimization problem of determining both the quality level at which to make their product (how
expensive ingredients to use) and the price at which to sell it. We analyze optimal seller strategies
as well as analogs of Walrasian equilibria in this setting. A key question we are interested in is:
to what extent will the price of a good be a reliable indicator of the good’s quality?
One result we show is that indeed in this model, price will be a surprisingly robust signal for
quality under optimal seller behavior. In particular, while the specific quality and price that a
seller should choose will depend highly on the specific distribution of buyers, for optimal sellers,
price and quality will be linearly related, independent of that distribution. We also show that
for the case of multiple buyers and sellers, an analog of Walrasian equilibrium exists in this
setting, and can be found via a natural tatonnement process. Finally, we analyze markets with
a combination of “locals” (who know the quality of each good) and “tourists” (who do not) and
analyze under what conditions the market will become a tourist trap, setting quality to zero
while keeping prices high.
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- NSF-PAR ID:
- 10057396
- Date Published:
- Journal Name:
- ITCS 2018
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
