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Data markets with dynamic arrival of buyers and sellers


Moor, Dmitry (2019). Data markets with dynamic arrival of buyers and sellers. In: The 14th Workshop on the Economics of Networks, Systems and Computation, Phoenix, Arizona, 28 July 2019 - 28 July 2019, 1-6.

Abstract

We propose a market design solution for a market for distributed data. The main challenges addressed by our solution are (1) different data providers produce different databases that can be joined to produce answers for users' queries; (2) data providers have high fixed costs for producing their databases; and (3) buyers and sellers can arrive dynamically to the market. Our design relies on using a Markov chain with states corresponding to different numbers of allocated databases. The transition probabilities between different states are governed by the payments suggested by the market platform to the data providers. The main challenge in this setting is to guarantee dynamic incentive compatibility, i.e., to ensure that buyers and sellers are not incentivized to arrive late to the market or to misreport their costs or values. To achieve this, we disentangle the payments suggested by the market platform to the sellers from the posted prices exposed to the buyers. We prove that the buyer-optimal payments that are exposed to sellers are non-increasing which prevents late arrivals of sellers. Further, we demonstrate that the posted prices exposed to buyers constitute a martingale process (i.e., late arrivals lead to the same expected price). Finally, we show that our design guarantees zero expected average budget deficit and we perform a number of simulations to validate our model.

Abstract

We propose a market design solution for a market for distributed data. The main challenges addressed by our solution are (1) different data providers produce different databases that can be joined to produce answers for users' queries; (2) data providers have high fixed costs for producing their databases; and (3) buyers and sellers can arrive dynamically to the market. Our design relies on using a Markov chain with states corresponding to different numbers of allocated databases. The transition probabilities between different states are governed by the payments suggested by the market platform to the data providers. The main challenge in this setting is to guarantee dynamic incentive compatibility, i.e., to ensure that buyers and sellers are not incentivized to arrive late to the market or to misreport their costs or values. To achieve this, we disentangle the payments suggested by the market platform to the sellers from the posted prices exposed to the buyers. We prove that the buyer-optimal payments that are exposed to sellers are non-increasing which prevents late arrivals of sellers. Further, we demonstrate that the posted prices exposed to buyers constitute a martingale process (i.e., late arrivals lead to the same expected price). Finally, we show that our design guarantees zero expected average budget deficit and we perform a number of simulations to validate our model.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Computational Theory and Mathematics
Physical Sciences > Computer Networks and Communications
Language:English
Event End Date:28 July 2019
Deposited On:03 Oct 2019 13:37
Last Modified:22 Apr 2020 21:12
Publisher:ACM Press
ISBN:9781450368377
OA Status:Green
Publisher DOI:https://doi.org/10.1145/3338506.3340270
Other Identification Number:merlin-id:18566

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