Tackling real-world socio-economic challenges requires designing and testing economic
policies. However, this is hard in practice, due to a lack of appropriate (micro-level) …

Automated auction design aims to find empirically high-revenue mechanisms through
machine learning. Existing works on multi item auction scenarios can be roughly divided into …

Designing an incentive compatible auction that maximizes expected revenue is an intricate
task. The single-item case was resolved in a seminal piece of work by Myerson in 1981, but …

RegretNet is a recent breakthrough in the automated design of revenue-maximizing
auctions. It combines the flexibility of deep learning with the regret-based approach to relax …

We consider the problem of repeatedly auctioning a single item to multiple iid buyers who
each use a no-regret learning algorithm to bid over time. In particular, we study the seller's …

Abstract The 2020 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred
Nobel was awarded to Paul R. Milgrom and Robert B. Wilson for “improvements to auction …

Designing an incentive compatible auction that maximizes expected revenue is a central
problem in Auction Design. Theoretical approaches to the problem have hit some limits in …

Traditionally, the Bayesian optimal auction design problem has been considered either
when the bidder values are iid, or when each bidder is individually identifiable via her value …

Algorithms often have tunable parameters that impact performance metrics such as runtime
and solution quality. For many algorithms used in practice, no parameter settings admit …

We initiate the study of computing (near-) optimal contracts in succinctly representable
principal-agent settings. Here optimality means maximizing the principal's expected payoff …