Abstract—The intuition that profit is optimized by maximiz-ing marginal revenue is a guiding principle in microeconomics. In the classical auction theory for agents with quasi-linear utility and single-dimensional preferences, Bulow and Roberts [1] show that the optimal auction of Myerson [2] is in fact optimizing marginal revenue. In particular Myerson’s virtual values are exactly the derivative of an appropriate revenue curve. This paper considers mechanism design in environments where the agents have multi-dimensional and non-linear pref-erences. Understanding good auctions for these environments is considered to be the main challenge in Bayesian opti-mal mechanism design. In these environments maximizing marginal revenue may not be opti...
This survey describes the approximately optimal mechanism design paradigm and uses it to in-vestigat...
We study a fundamental problem in micro economics called optimal auction design: A seller wishes to ...
Thesis (Ph.D.)--University of Washington, 2019The data used as input for many algorithms today comes...
The intuition that profit is optimized by maximizing marginal revenue is a guiding principle in micr...
Bayesian auction design investigates how to sell scarce resources to agents with private values draw...
This thesis studies the design of Bayesian revenue-optimal auctions for a class of problems in which...
This thesis studies the design of Bayesian revenue-optimal auctions for a class of problems in which...
Myerson’s 1981 characterization of revenue-optimal auctions for single-dimensional agents follows fr...
Consider a seller with multiple digital goods or services for sale, such as movies, soft-ware, or ne...
Consider a seller with multiple digital goods or services for sale, such as movies, soft-ware, or ne...
Consider a seller with multiple digital goods or services for sale, such as movies, software, or net...
Consider a seller with multiple digital goods or services for sale, such as movies, software, or net...
In this lecture we continue our study of revenue-maximization in multi-parameter problems. Unlike Le...
Abstract. One of the most fundamental problems in mechanism design is that of designing the auction ...
This survey describes the approximately optimal mechanism design paradigm and uses it to in-vestigat...
This survey describes the approximately optimal mechanism design paradigm and uses it to in-vestigat...
We study a fundamental problem in micro economics called optimal auction design: A seller wishes to ...
Thesis (Ph.D.)--University of Washington, 2019The data used as input for many algorithms today comes...
The intuition that profit is optimized by maximizing marginal revenue is a guiding principle in micr...
Bayesian auction design investigates how to sell scarce resources to agents with private values draw...
This thesis studies the design of Bayesian revenue-optimal auctions for a class of problems in which...
This thesis studies the design of Bayesian revenue-optimal auctions for a class of problems in which...
Myerson’s 1981 characterization of revenue-optimal auctions for single-dimensional agents follows fr...
Consider a seller with multiple digital goods or services for sale, such as movies, soft-ware, or ne...
Consider a seller with multiple digital goods or services for sale, such as movies, soft-ware, or ne...
Consider a seller with multiple digital goods or services for sale, such as movies, software, or net...
Consider a seller with multiple digital goods or services for sale, such as movies, software, or net...
In this lecture we continue our study of revenue-maximization in multi-parameter problems. Unlike Le...
Abstract. One of the most fundamental problems in mechanism design is that of designing the auction ...
This survey describes the approximately optimal mechanism design paradigm and uses it to in-vestigat...
This survey describes the approximately optimal mechanism design paradigm and uses it to in-vestigat...
We study a fundamental problem in micro economics called optimal auction design: A seller wishes to ...
Thesis (Ph.D.)--University of Washington, 2019The data used as input for many algorithms today comes...