4 research outputs found
Adversarial learning for revenue-maximizing auctions
We introduce a new numerical framework to learn optimal bidding strategies in
repeated auctions when the seller uses past bids to optimize her mechanism.
Crucially, we do not assume that the bidders know what optimization mechanism
is used by the seller. We recover essentially all state-of-the-art analytical
results for the single-item framework derived previously in the setup where the
bidder knows the optimization mechanism used by the seller and extend our
approach to multi-item settings, in which no optimal shading strategies were
previously known. Our approach yields substantial increases in bidder utility
in all settings. Our approach also has a strong potential for practical usage
since it provides a simple way to optimize bidding strategies on modern
marketplaces where buyers face unknown data-driven mechanisms
Fairness in Selection Problems with Strategic Candidates
To better understand discriminations and the effect of affirmative actions in
selection problems (e.g., college admission or hiring), a recent line of
research proposed a model based on differential variance. This model assumes
that the decision-maker has a noisy estimate of each candidate's quality and
puts forward the difference in the noise variances between different
demographic groups as a key factor to explain discrimination. The literature on
differential variance, however, does not consider the strategic behavior of
candidates who can react to the selection procedure to improve their outcome,
which is well-known to happen in many domains.
In this paper, we study how the strategic aspect affects fairness in
selection problems. We propose to model selection problems with strategic
candidates as a contest game: A population of rational candidates compete by
choosing an effort level to increase their quality. They incur a cost-of-effort
but get a (random) quality whose expectation equals the chosen effort. A
Bayesian decision-maker observes a noisy estimate of the quality of each
candidate (with differential variance) and selects the fraction of
best candidates based on their posterior expected quality; each selected
candidate receives a reward . We characterize the (unique) equilibrium of
this game in the different parameters' regimes, both when the decision-maker is
unconstrained and when they are constrained to respect the fairness notion of
demographic parity. Our results reveal important impacts of the strategic
behavior on the discrimination observed at equilibrium and allow us to
understand the effect of imposing demographic parity in this context. In
particular, we find that, in many cases, the results contrast with the
non-strategic setting.Comment: Accepted for publication in the proceedings of the Twenty-Third ACM
Conference on Economics and Computation (EC'22
Automated Mechanism Design for Classification with Partial Verification
We study the problem of automated mechanism design with partial verification,
where each type can (mis)report only a restricted set of types (rather than any
other type), induced by the principal's limited verification power. We prove
hardness results when the revelation principle does not necessarily hold, as
well as when types have even minimally different preferences. In light of these
hardness results, we focus on truthful mechanisms in the setting where all
types share the same preference over outcomes, which is motivated by
applications in, e.g., strategic classification. We present a number of
algorithmic and structural results, including an efficient algorithm for
finding optimal deterministic truthful mechanisms, which also implies a faster
algorithm for finding optimal randomized truthful mechanisms via a
characterization based on convexity. We then consider a more general setting,
where the principal's cost is a function of the combination of outcomes
assigned to each type. In particular, we focus on the case where the cost
function is submodular, and give generalizations of essentially all our results
in the classical setting where the cost function is additive. Our results
provide a relatively complete picture for automated mechanism design with
partial verification.Comment: AAAI'2