Bayesian Mechanism Design with Efficiency, Privacy, and Approximate Truthfulness

Frugality and Truthfulness in Approximate Mechanism Design

Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge

We study minimal single-task peer prediction mechanisms that have limited knowledge about agents’ beliefs. Without knowing what agents’ beliefs are or eliciting additional information, it is not possible to design a truthful mechanism in a Bayesian-Nash sense. We go beyond truthfulness and explore equilibrium strategy profiles that are only partially truthful. Using the results from the multi-a...

Egalitarianism of Random Assignment Mechanisms

We consider the egalitarian welfare of random assignment mechanisms when agents have unrestricted cardinal utilities over the objects. We define and give bounds on how well different random assignment mechanisms approximate the optimal egalitarian value (OEV) and investigate the effect that different well-known properties like ordinality, envyfreeness, and truthfulness have on the achievable eg...

Bayesian inference under differential privacy

Bayesian inference is an important technique throughout statistics. The essence of Beyesian inference is to derive the posterior belief updated from prior belief by the learned information, which is a set of differentially private answers under differential privacy. Although Bayesian inference can be used in a variety of applications, it becomes theoretically hard to solve when the number of di...

Setting Lower Bounds on Truthfulness

We present general techniques for proving inapproximability results for several paradigmatic truthful multidimensional mechanism design problems. In particular, we demonstrate the strength of our techniques by exhibiting a lower bound of 2 − 1 m for the scheduling problem with m unrelated machines (formulated as a mechanism design problem in the seminal paper of Nisan and Ronen on Algorithmic M...