Optimal Economic Design Through Deep Learning (Short Paper)
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CitationDütting, Paul, Zhe Feng, Harikrishna Narasimhan, and David C. Parkes. 2017. Optimal Economic Design through Deep Learning (Short Paper). 31st Conference on Neural Information Processing Systems (NIPS), December 4-9, 2017, Long Beach, CA, USA.
AbstractDesigning an auction that maximizes expected revenue is an intricate task. Despite major efforts, only the single-item case is fully understood. We explore the use of tools from deep learning on this topic. The design objective is revenue optimal, dominant-strategy incentive compatible auctions. For a baseline, we show that multi-layer neural networks can learn almost-optimal auctions for a variety of settings for which there are analytical solutions, and even without encoding characterization results into the design of the network. Our research also demonstrates the potential that deep nets have for deriving auctions with high revenue for poorly understood problems.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41718766
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