Mechanism Design

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10 papers in the last four weeks, up 150% on the four weeks before. 0.1% of all new papers.

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Data Market Design through Deep Learning
Oct 31, 2023Sai Srivatsa Ravindranath, Yanchen Jiang, David C. ParkesMechanism Design Information Design
Harvard John A. Paulson School of Engineering and Applied Sciences
Incentives to Offer Algorithmic Recourse
Jan 27, 2023Matthew Olckers, Toby WalshMechanism Design Algorithmic Recourse
Centre for Household Finance, Stellenbosch University · UNSW Sydney
Stackelberg POMDP: Learning to Lead via Reinforcement Learning
Oct 7, 2022Matthias Gerstgrasser, Gianluca Brero, Alon Eden +6Reinforcement Learning Multi-Agent Reinforcement Learning
John A. Paulson School of Engineering and Applied Sciences, Harvard University · Data Science Initiative, Brown University · School of Computer Science and Engineering, Hebrew University of Jerusalem +2

Oct 31, 2023, cs.GTJ/K move · Enter open · S save

Data Market Design through Deep Learning

Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes

Harvard John A. Paulson School of Engineering and Applied Sciences

Mechanism Design Information Design

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The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller, where each experiment reveals some of the information known to a seller and has a corresponding price [Bergemann et al., 2018]. Each buyer has their own decision to make in a world environment, and their subjective expected value for the information associated with a particular experiment comes from the improvement in this decision and depends on their prior and value for different outcomes. In a setting with multiple buyers, a buyer's expected value for an experiment may also depend on the information sold to others [Bonatti et al., 2022]. We introduce the application of deep learning for the design of revenue-optimal data markets, looking to expand the frontiers of what can be understood and achieved. Relative to earlier work on deep learning for auction design [Dütting et al., 2023], we must learn signaling schemes rather than allocation rules and handle obedience constraints

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these arising from modeling the downstream actions of buyers

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in addition to incentive constraints on bids. Our experiments demonstrate that this new deep learning framework can almost precisely replicate all known solutions from theory, expand to more complex settings, and be used to establish the optimality of new designs for data markets and make conjectures in regard to the structure of optimal designs.

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Latest papers 93

Data Market Design through Deep Learning

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