Data Market Design through Deep Learning
- URL: http://arxiv.org/abs/2310.20096v1
- Date: Tue, 31 Oct 2023 00:21:09 GMT
- Title: Data Market Design through Deep Learning
- Authors: Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes
- Abstract summary: We introduce the application of deep learning for the design of revenue-optimal data markets.
Our experiments demonstrate that this new deep learning framework can almost precisely replicate all known solutions from theory.
- Score: 16.505791601397185
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The $\textit{data market design}$ 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 [D\"utting et al., 2023], we
must learn signaling schemes rather than allocation rules and handle
$\textit{obedience constraints}$ $-$ these arising from modeling the downstream
actions of buyers $-$ 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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