8 citations · 27 across the 6 of their papers we have counts for
6 papers · 1 filter
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning
Michael Curry, Alexander Trott, Soham Phade +2
Real economies can be modeled as a sequential imperfect-information game with many heterogeneous agents, such as consumers, firms, and governments. Dynamic general equilibrium (DGE…
Differentiable Economics for Randomized Affine Maximizer Auctions
Michael Curry, Tuomas Sandholm, John Dickerson
A recent approach to automated mechanism design, differentiable economics, represents auctions by rich function approximators and optimizes their performance by gradient descent. T…
Learning Revenue-Maximizing Auctions With Differentiable Matching
Michael J. Curry, Uro Lyi, Tom Goldstein +1
We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations. Our architecture uses the Sinkhorn algorithm to perf…
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
Neehar Peri, Michael J. Curry, Samuel Dooley +1
The design of optimal auctions is a problem of interest in economics, game theory and computer science. Despite decades of effort, strategyproof, revenue-maximizing auction designs…
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
Kevin Kuo, Anthony Ostuni, Elizabeth Horishny +5
The design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectru…
Certifying Strategyproof Auction Networks
Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein +1
Optimal auctions maximize a seller's expected revenue subject to individual rationality and strategyproofness for the buyers. Myerson's seminal work in 1981 settled the case of auc…