8 research outputs found
Envy, Regret, and Social Welfare Loss
Incentive compatibility (IC) is a desirable property for any auction mechanism, including those used in online advertising. However, in real world applications practical constraints and complex environments often result in mechanisms that lack incentive compatibility. Recently, several papers investigated the problem of deploying black-box statistical tests to determine if an auction mechanism is incentive compatible by using the notion of IC-Regret that measures the regret of a truthful bidder. Unfortunately, most of those methods are computationally intensive, since they require the execution of many counterfactual experiments. In this work, we show that similar results can be obtained using the notion of IC-Envy. The advantage of IC-Envy is its efficiency: it can be computed using only the auction's outcome. In particular, we focus on position auctions. For position auctions, we show that for a large class of pricing schemes (which includes e.g. VCG and GSP), IC-Envy ≥ IC-Regret (and IC-Envy = IC-Regret under mild supplementary conditions). Our theoretical results are completed showing that, in the position auction environment, IC-Envy can be used to bound the loss in social welfare due to the advertiser untruthful behavior. Finally, we show experimentally that IC-Envy can be used as a feature to predict IC-Regret in settings not covered by the theoretical results. In particular, using IC-Envy yields better results than training models using only price and value features
Thresholding at the monopoly price: an agnostic way to improve bidding strategies in revenue-maximizing auctions
We address the problem of improving bidders' strategies in prior-dependent
revenue-maximizing auctions. We introduce a simple and generic method to design
novel bidding strategies if the seller uses past bids to optimize her
mechanism. This strategy works with general value distributions, with
asymmetric bidders and for different revenue-maximizing mechanisms.
Furthermore, it can be made robust to sample approximation errors on the seller
part. This results in a large increase in utility for bidders whether they have
a full or partial knowledge of their competitors. In the case where the buyer
has no information about the competition, we propose a simple and agnostic
strategy that is robust to mechanism changes and local (as opposed to global)
optimization of e.g. reserve prices by the seller. In textbook-style examples,
for instance with uniform value distributions and two bidders, this
no-side-information and mechanism-independent strategy yields an enormous 57%
increase in buyer utility for lazy second price auctions with no reserves. In
the i.i.d symmetric case, we show existence and uniqueness of a Nash
equilibrium in the class of strategy we consider for lazy second price
auctions, as well as the corresponding explicit shading strategies. Our
approach also works for Myerson auctions for instance. At this Nash
equilibrium, buyer's utility is the same as in a second price auction with no
reserve. Our approach also yields optimal solutions when buyer are constrained
in the class of shading strategies they can use, a realistic constraint in
practical applications. The heart of our approach is to see optimal auctions in
practice as a Stackelberg game where the buyer is the leader, as he is the
first one to move (here bid) when the seller is the follower as she has no
prior information on the bidder